Cluster analysis method, cluster analysis system, and cluster analysis program

Abstract

A server 4 performs: a set extraction step (S1) for extracting a set of documents from among a plurality of documents in accordance with a condition that uses time information; an inter-document similarity degree calculation step (S2) for calculating the degree of inter-document similarity between the content of one document and the content of another document in the set; a cluster classification step (S3) for classifying documents in the set into a plurality of clusters, each including similar documents, on the basis of degrees of inter-document similarity; an inter-cluster similarity degree calculation step (S6) for calculating the degrees of inter-cluster similarity between clusters of a plurality of sets; and a cluster association step (S7) for generating, on the basis of the degrees of inter-cluster similarity, association information associating clusters that are related to each other and that belong to different sets.

Description

[Technical field]

[0001] The present invention relates to a cluster analysis method, a cluster analysis system, and a cluster analysis program that classify a plurality of documents into clusters according to their contents and generate display data showing the association between the clusters according to a time series. [Background technology]

[0002] Traditionally, when analyzing a large number of academic papers, literature, and other documents, a person would read the documents and classify them by content or create a summary. Human analysis takes time, and when multiple people analyze the documents, the accuracy of the classification and summary creation tends to vary depending on the experience and knowledge of the workers.

[0003] In addition, while complex and highly specialized documents such as academic papers require advanced expertise to understand their contents, there is a demand for people without such expertise to be able to easily obtain, understand, and use the latest information.

[0004] For example, a cluster analysis method has been proposed in which morphological analysis is performed on technical documents retrieved by a concept search, weights are assigned to each word obtained from the analysis, each technical document is converted into a vector, and technical documents with vectors with similar directions are grouped together into a single cluster (for example, Patent Document 1).

[0005] While such techniques can categorize information into clusters, they do not yet allow for the generation of clusters based on different time axes or the understanding of the relationships between different clusters. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2005-92443 A Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention aims to provide a cluster analysis method, a cluster analysis system, and a cluster analysis program that classify a large number of documents, particularly an enormous number of documents, into clusters composed of similar documents and enable understanding of relationships between clusters in other sets, such as the chronological relationships between clusters, thereby enabling understanding of relationships between clusters across sets. [Means for solving the problem]

[0008] That is, the present invention is a cluster analysis method in which a computer classifies a plurality of documents into clusters according to their contents, the method including a first set extraction step of extracting a first set from the plurality of documents under a first condition, a first document similarity calculation step of calculating document similarity between the contents of one document included in the first set and the contents of another document included in the first set, a first cluster classification step of classifying each document in the first set into a plurality of clusters based on the document similarity calculated in the first similarity calculation step, a second set extraction step of extracting a second set from the plurality of documents under a second condition different from the first condition, and a first cluster classification step of classifying each document in the first set into a plurality of clusters based on the document similarity calculated in the first similarity calculation step. a second inter-document similarity calculation step of calculating an inter-document similarity between the contents of a document included in a second set and other documents included in the second set; a second cluster classification step of classifying each document into a plurality of clusters based on the inter-document similarity calculated in the second similarity calculation step; an inter-cluster similarity calculation step of calculating an inter-cluster similarity between a cluster classified in the first cluster classification step and a cluster classified in the second cluster classification step; and a cluster association step of generating association information linking related clusters across the first set and the second set based on the inter-cluster similarity calculated in the inter-cluster similarity calculation step. Effect of the Invention

[0009] This invention classifies a large number of documents, particularly a huge number of documents, into document groups (clusters) consisting of similar documents, and enables the relationships between clusters in other sets, such as the chronological relationships between clusters, to be understood, thereby making it possible to understand the relationships between clusters. [Brief description of the drawings]

[0010] [Figure 1] 1 is an overall configuration diagram of a cluster analysis system according to an embodiment of the present invention. [Diagram 2] 13 is a display example of a cluster analysis result displayed on an output unit of an information terminal. [Diagram 3] FIG. 4 is an explanatory diagram of display data. [Figure 4] FIG. 13 is an explanatory diagram showing the relationship between clusters across sets. [Diagram 5] FIG. 13 is an explanatory diagram showing an example of a time series map of each cluster. [Figure 6] 4 is a flowchart showing a cluster analysis control routine executed by a server of the cluster analysis system in one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0012] FIG. 1 is a diagram showing the overall configuration of a cluster analysis system according to an embodiment of the present invention, and the configuration of this embodiment will be described with reference to this figure.

[0013] As shown in FIG. 1, in the cluster analysis system 1 according to this embodiment, a document database 2 (hereinafter, database will be abbreviated as "DB"), an information terminal 3, and a server 4 are connected via a communication network N. The communication network N is, for example, the Internet, an intranet, a VPN (Virtual Private Network), etc., and is a communication network capable of transmitting information bidirectionally using wired or wireless communication means. Also, in FIG. 1, for the sake of simplicity of explanation, one document DB 2 and one information terminal 3 are connected to one server 4, but the server 4 can be connected to multiple document DBs and multiple information terminals 3.

[0014] The document DB2 is a database that stores information on documents such as academic papers, patent documents, magazines, books, and newspaper articles, and the stored documents are made available to limited or unlimited persons. In this embodiment, the document DB2 will be described as an example of a document DB that stores information on medical literature. However, there is no limit to the contents, fields, and types of documents that can be stored in the document DB of the present invention. In this embodiment, the information on medical literature includes bibliographic items such as the author's name, publication date (time information), and the author's affiliated institution, content items of the paper such as the title, abstract, and main text of the paper, citation and cited information such as the number of citations and cited documents and the name of the document, publication information such as the name of the academic society, magazine, or publisher in which the document was published, etc.

[0015] The information terminal 3 is, for example, a personal computer (hereinafter, referred to as a “PC”), a smartphone, a tablet PC, or a mobile terminal such as a mobile phone.

[0016] The output unit 10 is, for example, a device such as a display or a printer, and is capable of visually displaying the display data generated by the server 4.

[0017] The input unit 11 is a device such as a keyboard or a mouse, and is capable of inputting and operating information. The output unit 10 and the input unit 11 may be integrated together to form, for example, a touch panel.

[0018] A person (user) who uses the information terminal 3 can check the display data generated by the server 4 on the output unit 10 and can also issue various instructions to the server 4 via the input unit 11.

[0019] The server 4 is composed of one or more servers (computers) that classify a plurality of documents into clusters according to their contents and generate display data showing the relationships between the documents. The server 4 has various calculation units and storage units, such as a document storage unit 20, a set extraction unit 21, an inter-document similarity calculation unit 22, a cluster classification unit 23, an index calculation unit 24, a network storage unit 25, an inter-cluster similarity calculation unit 26, a cluster association unit 27, and a display data generation unit 28.

[0020] In detail, the document storage unit 20 is a storage unit that is connected to the document DB2 via the communication network N and acquires and stores information on necessary documents from the document DB2. For example, in this embodiment, medical literature is acquired from the document DB2 and stored. The document storage unit 20 also has a function of automatically updating documents in the document storage unit 20 in synchronization with updates such as addition or deletion of documents in the document DB2.

[0021] The set extraction unit 21 has a function of extracting a set from the document storage unit 20 under a condition using time information. For example, the set extraction unit 21 can extract a set limited to medical literature published in a predetermined period (for example, a predetermined year) using the publication date of the document. The condition for extracting a set is not limited to time information, and other conditions may be used or other conditions may be added. For example, conditions such as medical literature related to a specific disease, medical literature presented at a specific academic conference, etc. may be used or added, or a plurality of these conditions may be used. Furthermore, it is also possible to narrow down the number of documents included in one set to a predetermined number. Furthermore, when a document is updated in the document storage unit 20, the set extraction unit 21 extracts documents that meet the condition again based on the updated information.

[0022] The document similarity calculation unit 22 has a function of calculating the similarity between the contents of one document and the contents of another document in the set extracted by the set extraction unit 21. For example, TF-IDF or cosine similarity can be used to calculate the similarity. That is, the document similarity calculation unit 22 extracts words used in the contents of each document, weights each word based on the product of the frequency of occurrence in the document (TF: Term Frequency) and the rarity of the word used in other documents (IDF: Inverse Document Frequency), and vectorizes the document. Then, the document similarity calculation unit 22 calculates the cosine (cos) value between the vectorized documents as the value of the similarity between the documents. For example, the similarity between the first document and the second document is 0.856, and the similarity between the first document and the third document is 0.732. The similarity is expressed as a value between 0 and 1, and the closer to 1 the value is, the more similar the documents are.

[0023] The cluster classification unit 23 generates a network including each document connected by lines (hereinafter referred to as "edges") based on the similarity calculated by the inter-document similarity calculation unit 22, and classifies similar documents into clusters (document groups). The clustering algorithm is not particularly limited, but for example, an algorithm (the so-called Girvan-Newman algorithm) that specifies clusters by iterative calculations in which connectivity between nodes is maintained as much as possible even if edges are cut off can be used.

[0024] The index calculation unit 24 has a function of calculating a centrality index indicating the centrality of each document in the network generated by the cluster classification unit 23. The algorithm for calculating the centrality index is not particularly limited, and for example, eigenvector centrality, PageRank, betweenness centrality, degree centrality, etc. can be used. In this embodiment, eigenvector centrality is used. Eigenvector centrality is indicated by the probability that a document (hereinafter referred to as a "node") in a network will pass through that node when edges are repeatedly traced starting from an arbitrary node in the network.

[0025] The network storage unit 25 is a storage unit that stores network information after clustering for each set of documents extracted by the set extraction unit 21. For example, when the set extraction unit 21 generates sets for each year based on the publication year of the documents, the network information for each year is stored in the network storage unit 25. Each piece of network information stored here is converted into network display data by the display data generation unit 28 and can be displayed on the output unit 10 of the information terminal 3.

[0026] Fig. 2 is an example of a network displayed as a cluster analysis result on the output section of an information terminal, and Fig. 3 is an explanatory diagram of the network. The display of a network in a set will be explained based on these figures.

[0027] As shown in Figures 2 and 3, a network for a set is represented by a representation for each document in the set according to its centrality index, the type of cluster, and the degree of similarity between each document.

[0028] Specifically, as shown in Figure 3, each document (node) on the network is represented by a circle, the centrality index is represented by the size of the circle, the type of cluster is represented by color, and the degree of similarity is represented by the thickness of the edge.

[0029] In Fig. 3, ten nodes 30a to 30j (hereinafter collectively referred to as "nodes 30") are displayed, with the four nodes 30a to 30d at the top left belonging to a first cluster, and the six nodes 30e to 30j at the bottom right belonging to a second cluster. The first cluster and the second cluster can be shown in different colors. In Fig. 3, the difference in color is shown by different hatching.

[0030] The size of a node 30 indicates the degree of centrality, and in Fig. 3, it can be seen that nodes 30a and 30e are documents with high centrality. In addition, the thickness of an edge 32 connecting nodes 30 indicates the degree of similarity between documents connected by that edge 32. Therefore, in Fig. 3, it can be seen that the document similarity between these nodes is high because the edge 32 between node 30a and node 30c and between node 30e and node 30h is thick.

[0031] The network storage unit 25 stores network information on which such network display is based for each set.

[0032] The inter-cluster similarity calculation unit 26 has a function of calculating inter-cluster similarity between clusters of a plurality of sets stored in the network storage unit 25. For the calculation of the inter-cluster similarity, TF-IDF or cosine similarity can be used as in the inter-document similarity calculation unit 22. That is, the inter-cluster similarity calculation unit 26 extracts words used in the contents of documents in each cluster in each set, weights each word based on the product of the occurrence frequency (TF: Term Frequency) in the cluster and the rarity (IDF: Inverse Document Frequency) of the word used in other clusters, and vectorizes each cluster. Then, the inter-cluster similarity calculation unit 26 calculates the cosine (cos) value between the cluster vectorized in the first set and the cluster vectorized in the second set as the value of the inter-cluster similarity.

[0033] The cluster associating unit 27 has a function of generating cluster association information by regarding clusters having an inter-cluster similarity equal to or greater than a predetermined threshold as related clusters. In other words, the cluster associating unit 27 links related clusters across sets.

[0034] The display data generation unit 28 is capable of generating network display data based on the network information stored in the network memory unit 25 described above, and has the function of generating time-series display data showing the relationship between clusters across sets associated in the cluster association unit 27.

[0035] FIG. 4 shows the relationships between clusters across sets, and FIG. 5 shows an example of time-series display data.

[0036] Figure 4 shows the network in the set shown in Figure 3 above as an example of a network showing a set of medical literature published in 2018. In addition, Figure 4 also shows a chronological arrangement of networks showing sets of medical literature published in 2017 and 2016.

[0037] As shown by the solid and dotted lines extending between the sets in Fig. 4, the inter-cluster similarity calculation unit 26 calculates the similarity between clusters across sets from the similarity between documents in a cluster of the set for 2018 and documents in a cluster of the set for 2017. In addition, the inter-cluster similarity calculation unit 26 can calculate the similarity between clusters in a chronological order by performing the same process for the set for 2017 and the set for 2016.

[0038] The time series display in Figure 5 shows the major clusters in the set of medical literature published each year from 2014 to 2018, arranged in chronological order. Clusters are shown as circles, with the size of the circle representing the number of papers belonging to the cluster, and the number written inside the circle indicating the number of papers.

[0039] In Figure 5, cluster associations are performed based on the most recent year, 2018. The four clusters 40a to 40d with the largest number of publications in 2018 are displayed, and the associations with past clusters based on these clusters are shown with lines (edges 50, 51). As in Figure 3, each cluster is shown in a different color, but in Figure 5, the differences in color are shown with different hatching.

[0040] The thickness of the edges 50, 51 indicates the similarity between the clusters, and the display data generating unit 28 generates display data so as to display only similarities equal to or greater than a predetermined threshold. There are two types of edges: a main edge 50 that connects a cluster with the highest similarity to a reference cluster, and a sub-edge 51 that connects other clusters with the second or subsequent similarities. Clusters connected by the main edge 50 are shown in the same color (hatched) as they are clusters with the same attribute. On the other hand, the sub-edges 51 connect clusters with different attributes. In medical literature, the attribute of a cluster corresponds to, for example, a research theme.

[0041] In this way, if Figure 4, in which the clusters for each year are connected by edges 50 and 51, were time-series data from medical literature, the following can be inferred.

[0042] For example, the attributes of cluster 40a, which had the highest number of publications in 2018, were also first in 2017 and 2016 (clusters 41a and 42a), but were second in 2015 and 2014 (clusters 43a and 44a), with the number of publications increasing sharply from 2015 to 2016. Therefore, it can be inferred that while the research topic of cluster 40a has traditionally attracted attention, something even more noteworthy has occurred between 2015 and 2016.

[0043] On the other hand, cluster 40b, which was ranked second in terms of the number of publications in 2018, saw a decrease in the number of publications from 2015 to 2016, suggesting that a treatment for the research topic in cluster 40b was established during this period. In addition, this research topic was connected to clusters 43c and 40c, which were ranked third in terms of the number of publications, by sub-edge 51 from 2014 to 2015 and from 2017 to 2018, suggesting that the research topic has branched out.

[0044] In addition, Cluster 40c, which ranked third in terms of the number of publications in 2018, has consistently been ranked third since 2014, but the number of publications has been increasing, suggesting that this is a research topic that has the potential to continue to develop in the future.

[0045] Cluster 40d, which ranked fourth in number of publications in 2018, is an attribute that emerged in 2017, indicating that it is a relatively new research topic. Furthermore, it can be inferred that clusters 42d, 43d, and 44d, which ranked fourth in number of publications from 2016 to 2014, were integrated into cluster 41b, which ranked second in number of publications, in 2017.

[0046] In this way, by showing the relationships between clusters across sets, it becomes possible to see the evolution of clusters.

[0047] The display data generating unit 28 transmits the generated network display data and time-series display data to the information terminal 3 connected to the server 4 via the communication network N.

[0048] In the cluster analysis system 1 configured in this manner, for example, when a user inputs information regarding medical literature, such as the name of a specific disease, to the server 4 via the input unit 11 of the information terminal 3, the server 4 outputs network display data such as that shown in Figures 2 and 3 or time-series display data such as that shown in Figure 5 in accordance with the input information to the output unit 10 of the information terminal 3.

[0049] 6 shows a flowchart of a cluster analysis routine for generating time-series display data executed by the server 4 of the cluster analysis system 1. The cluster analysis method of this embodiment will be described in detail below with reference to this flowchart.

[0050] When the server 4 receives input information such as a specific disease name, a time series period, and how to divide the period from the information terminal 3, in step S1, the set extraction unit 21 extracts a set of documents that meet the conditions from the document storage unit 20. For example, when the time series display of Fig. 5 described above is requested, first, a set of medical documents published in 2018 (first set) is extracted.

[0051] In the following step S2, the inter-document similarity calculation unit 22 calculates the inter-document similarity between the documents constituting the set extracted in step S1.

[0052] In step S3, the cluster classification unit 23 generates a network between documents based on the similarity calculated in step S2, and classifies the documents so that a set of similar documents constitutes a cluster.

[0053] In step S4, the index calculation unit 24 calculates a centrality index indicating the centrality of the document in the network generated in step S3. As a result, network information related to the set extracted in step S1 is generated and stored in the network storage unit 25.

[0054] In step S5, the inter-cluster similarity calculation unit 26 judges whether or not a network of a set that meets the conditions is stored in the network storage unit 25. If the judgment result is false (No), the process returns to step S1. For example, in the case of the time series display in FIG. 5 described above, if a network has not been generated for the set of each year from 2014 to 2018, the process returns to step S1, extracts the set of years that has not been generated, and executes steps S2 to S4 described above to generate a network.

[0055] If the result of the determination in step S5 is true (Yes), that is, if a network of sets that meet the conditions has been generated, the process proceeds to step S6.

[0056] In step S6, the inter-cluster similarity calculation unit 26 calculates the inter-cluster similarity between clusters of a plurality of sets stored in the network storage unit 25. For example, in the case of the time series display in Fig. 5, the inter-cluster similarity between clusters of the sets of 2018 and 2017 is calculated, and then the inter-cluster similarity between clusters of the sets of 2017 and 2016, 2016 and 2015, and 2015 and 2014 is calculated.

[0057] In step S7, the cluster associating unit 27 regards clusters having an inter-cluster similarity equal to or greater than a predetermined threshold as related clusters and generates cluster association information. For example, in the case of the time series display of Fig. 5, clusters having an inter-cluster similarity equal to or greater than a predetermined threshold for each year are connected by edges 50 and 51.

[0058] In step S8, the display data generating unit 28 generates time-series data such as that shown in FIG. 5, transmits it to the information terminal 3, and then ends this routine.

[0059] As described above, the cluster analysis system 1 in this embodiment extracts multiple sets with different temporal conditions, forms a network based on the document similarity within the sets, forms clusters of similar documents, calculates the similarity between clusters, and associates clusters across sets. This makes it possible to show the transition of clusters over time.

[0060] In addition, by associating clusters only with clusters whose inter-cluster similarity is equal to or greater than a predetermined threshold, unnecessary information can be reduced, the processing burden on the server 4 can be lightened, and the amount of information sent to the information terminal 3 can be reduced.

[0061] Furthermore, by generating time-series display data showing the relationships between clusters across associated sets, as shown in FIG. 5, it is possible to provide an overview of the changes in the clusters.

[0062] Thus, according to this embodiment, a large number of documents, particularly a huge number of documents, are classified into clusters consisting of similar documents, and the chronological relationships between each cluster can be grasped, thereby making it possible to understand the history between the clusters.

[0063] Although one embodiment of the present invention has been specifically described above, it will be understood that the present invention is not limited to this embodiment, and various changes and modifications therein can be made by those skilled in the art without departing from the scope or spirit of the present invention defined in the appended claims.

[0064] In the above embodiment, the display data generation unit 28 represented the time series display as shown in FIG. 5, with clusters represented by circles, the number of documents represented by the size of the circle, and the similarity between clusters represented by the thickness of the edge, but the representation of the time series display is not limited to this and may be shown in other ways.

[0065] In addition, in the cluster analysis system 1 in the above embodiment, the time-series relationship between clusters across sets can be grasped by using time information as a condition for extracting sets, but the condition for extracting sets is not limited to time information. For example, in the case of medical literature, the relationship between clusters related to diseases and medicines can be visualized by extracting sets using the target disease or type of medicine as a condition. Or, in the case of technical literature, the relationship between clusters related to a specific technology can be visualized by extracting sets using the technical field as a condition. In this way, by making it possible to grasp the relationship between clusters in various sets according to the condition for extracting sets, it is possible to understand the relationship between corresponding clusters in different sets. [Explanation of symbols]

[0066] 1. Cluster analysis system 2 Document DB 3. Information terminal 4 Server 10 Output section 11 Input section 20 Document storage unit 21 Set extraction part 22 Document Similarity Calculation Unit 23 Cluster Classification Section 24 Indicator calculation section 25 Network Storage 26 Inter-cluster similarity calculation section 27 Cluster Association Section 28 Display data generation unit

Claims

1. A cluster analysis method in which a computer classifies a plurality of documents, each of which includes at least time information, into clusters according to information on the documents from a database storing the plurality of documents, the plurality of documents being any of medical literature, academic papers, patent documents, magazines, books, and newspaper articles, the plurality of documents including at least time information, the plurality of documents comprising ... being classified into clusters according to information on the documents, the plurality of documents comprising the plurality of documents including at least time information a first set extraction step of extracting a first set from the plurality of documents under a first condition using the time information; a first inter-document similarity calculation step of calculating an inter-document similarity between a content of one document included in the first set and a content of another document included in the first set; a first cluster classification step of classifying documents included in the first set into a plurality of clusters based on the inter-document similarity calculated in the first similarity calculation step; a first index calculation step of calculating a centrality index indicating the centrality of each document in the cluster classified in the first cluster classification step, and generating network information related to the first set; a second set extraction step of extracting a second set from the plurality of documents using the time information under a second condition in which the time information is different from the first condition; a second inter-document similarity calculation step of calculating an inter-document similarity between a content of one document included in the second set and a content of another document included in the second set; a second cluster classification step of classifying documents included in the second set into a plurality of clusters based on the inter-document similarity calculated in the second similarity calculation step; a second index calculation step of calculating a centrality index indicating a centrality of each document in the cluster classified in the second cluster classification step, and generating network information related to the second set; an inter-cluster similarity calculation step of calculating an inter-cluster similarity between a cluster of network information related to a first set generated in the first index calculation step and a cluster of network information related to a second set generated in the second index calculation step; a cluster associating step of generating association information linking related clusters across the first set and the second set based on the inter-cluster similarity calculated in the inter-cluster similarity calculating step; a display data generating step of generating first network display data based on network information related to the first set generated in the first index calculation step, second network display data based on network information related to the second set generated in the second index calculation step, and time-series display data showing a relationship between clusters across the sets associated in the cluster associating step; Equipped with The display data generation step is a cluster analysis method in which the first network display data and the second network display data each represent a single document as a node, a centrality index represented by the size of the node, and the time series display data represents the first set and the second set arranged in time series.

2. 2. The cluster analysis method according to claim 1, wherein in the cluster associating step, clusters whose inter-cluster similarity calculated in the inter-cluster similarity calculating step is equal to or greater than a predetermined threshold are linked together.

3. 2. The cluster analysis method according to claim 1, wherein in the display data generation step, the clusters of the first set and the clusters of the second set are arranged in chronological order, and the time series display data is generated in which clusters that are related across the first set and the second set are connected by lines.

4. A cluster analysis system for classifying a plurality of documents into clusters according to information of the documents from a database storing information of the documents, the documents being any one of medical literature, academic papers, patent documents, magazines, books, and newspaper articles, each of which includes at least time information, the system comprising: a set extraction unit that extracts a first set from the plurality of documents under a first condition using the time information, and extracts a second set using the time information under a second condition in which the time information is different from the first condition; an inter-document similarity calculation unit that calculates an inter-document similarity between a content of one document included in the first set and a content of another document included in the first set, and calculates an inter-document similarity between a content of one document included in the second set and a content of another document included in the second set; a cluster classification unit that classifies the documents included in the first set into a plurality of clusters based on the document similarity calculated by the document similarity calculation unit, and classifies the documents included in the second set into a plurality of clusters based on the document similarity calculated by the document similarity calculation unit; an index calculation unit that calculates a centrality index indicating the centrality of each document in a cluster classified in the first set and generates network information related to the first set, and calculates a centrality index indicating the centrality of each document in a cluster classified in the second set and generates network information related to the second set; an inter-cluster similarity calculation unit that calculates an inter-cluster similarity between a cluster of network information related to the first set and a cluster of network information related to the second set; a cluster associating unit that generates association information linking related clusters across the first set and the second set based on the inter-cluster similarity calculated by the inter-cluster similarity calculating unit; a display data generation unit that generates first network display data based on network information related to the first set generated by the index calculation unit, second network display data based on network information related to the second set generated by the index calculation unit, and time-series display data showing a relationship between clusters across the sets associated by the cluster association unit; Equipped with The display data generation unit represents the first network display data and the second network display data with each document as a node, a centrality index represented by the size of the node, and represents the time series display data by arranging the first set and the second set in time series. This is a cluster analysis system.

5. A cluster analysis program for causing a computer to classify a plurality of documents, each of which includes at least time information, into clusters according to information of the documents from a database storing the plurality of documents, the plurality of documents being any of medical literature, academic papers, patent documents, magazines, books, and newspaper articles, the plurality of documents including at least time information, the plurality of documents comprising the plurality of documents being classified into clusters according to information of the documents, the plurality of documents comprising the plurality of documents being classified into clusters according to information of the documents, the plurality of documents comprising the plurality of documents being classified into clusters according to information of the documents, the plurality of documents comprising the plurality of documents being classified into clusters according to information of the documents, the plurality of documents comprising the plurality of documents including at least time information, ... a first set extraction step of extracting a first set from the plurality of documents under a first condition using the time information; a first inter-document similarity calculation step of calculating an inter-document similarity between a content of one document included in the first set and a content of another document included in the first set; a first cluster classification step of classifying documents included in the first set into a plurality of clusters based on the inter-document similarity calculated in the first similarity calculation step; a first index calculation step of calculating a centrality index indicating the centrality of each document in the cluster classified in the first cluster classification step, and generating network information related to the first set; a second set extraction step of extracting a second set from the plurality of documents using the time information under a second condition in which the time information is different from the first condition; a second inter-document similarity calculation step of calculating an inter-document similarity between a content of one document included in the second set and a content of another document included in the second set; a second cluster classification step of classifying documents included in the second set into a plurality of clusters based on the inter-document similarity calculated in the second similarity calculation step; a second index calculation step of calculating a centrality index indicating a centrality of each document in the cluster classified in the second cluster classification step, and generating network information related to the second set; an inter-cluster similarity calculation step of calculating an inter-cluster similarity between a cluster in the first network information generated in the first index calculation step and a cluster in the second network information generated in the second index calculation step; a cluster associating step of generating association information linking related clusters across the first set and the second set based on the inter-cluster similarity calculated in the inter-cluster similarity calculating step; a display data generating step of generating first network display data based on network information related to the first set generated in the first index calculation step, second network display data based on network information related to the second set generated in the second index calculation step, and time-series display data showing a relationship between clusters across the sets associated in the cluster associating step; Run the command, The display data generation step is an analysis program that represents the first network display data and the second network display data with each document as a node, a centrality index represented by the size of the node, and represents the time series display data by arranging the first set and the second set in chronological order.

6. A cluster analysis method in which a computer classifies a plurality of documents, each of which includes at least time information, into clusters according to information on the documents from a database storing the plurality of documents, the plurality of documents being any of medical literature, academic papers, patent documents, magazines, books, and newspaper articles, the plurality of documents including at least time information, the plurality of documents comprising ... being classified into clusters according to information on the documents, the plurality of documents comprising the plurality of documents including at least time information an index calculation step of calculating a centrality index indicating the centrality of each document in a cluster classified in a first set extracted from the plurality of documents under a condition using the time information and generating network information related to the first set, and calculating a centrality index indicating the centrality of each document in a cluster classified in a second set having time information different from the first set extracted from the plurality of documents and generating network information related to the second set; an inter-cluster similarity calculation step of calculating an inter-cluster similarity between a cluster of network information related to the first set generated in the index calculation step and a cluster of network information related to the second set; a cluster associating step of generating association information linking related clusters across the first set and the second set based on the inter-cluster similarity calculated in the inter-cluster similarity calculating step; a display data generating step of generating first network display data based on network information related to the first set generated in the index calculation step, second network display data based on network information related to the second set, and time-series display data showing a relationship between clusters across the sets associated in the cluster associating step; Equipped with The display data generation step is a cluster analysis method in which the first network display data and the second network display data each represent a single document as a node, a centrality index represented by the size of the node, and the time series display data represents the first set and the second set arranged in time series.

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