Labeling support system, labeling support method, and labeling support program

Abstract

In the present invention, a sorting means 191 generates a plurality of clusters by sorting data to be labelled by learning without a teacher. A searching means 192 searches, in each generated cluster, for commonalities among data items contained in such clusters. An output means 193 outputs, for each cluster, information related to the commonalities that were searched for.

Description

[Technical Field]

[0001] The present invention relates to a labeling support system, a labeling support method, and a labeling support program that support labeling of unlabeled data. [Background technology]

[0002] In the IoT (Internet of Things) society, it is now possible to collect data from various devices. Data classification is important for searching data and for learning AI (Artificial Intelligence) using the large amount of collected data.

[0003] In light of this situation, various methods for supporting data classification have been proposed. For example, Patent Document 1 describes a sensor data classification device that classifies sensor data obtained by a large number of sensors according to its characteristics. The device described in Patent Document 1 associates sets of sensor data divided into preset time intervals with sensor identifiers and division interval identifiers, and calculates multiple types of characteristic parameters from the data included in the divided data sets. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2016-99888 Summary of the Invention [Problem to be solved by the invention]

[0005] For example, when data features are extracted and classified into clusters, it is important to assign meaning to the clusters (labeling). However, labeling each clustered data is a very costly task, and the impact is particularly significant when the amount of data contained in the cluster is large.

[0006] Furthermore, if the data to be classified is video, for example, it takes time to check the data. Also, if the data to be classified contains data from multiple sensors, determining which data to focus on can be a tedious task.

[0007] In the device described in Patent Document 1, the calculation method of feature parameters for classification and the division intervals are determined in advance. However, even if data is classified based on values ​​calculated based on some criteria, there is still a problem in that it is costly to perform meaningful labeling work on unlabeled data.

[0008] Therefore, an object of the present invention is to provide a labeling support system, a labeling support method, and a labeling support program that can support the labeling work for clusters into which unlabeled data has been classified. [Means for solving the problem]

[0009] The labeling support system according to the present invention is characterized by comprising a classification means for generating a plurality of clusters by classifying data to be labeled using unsupervised learning, a search means for searching for common points among the data contained in each generated cluster, and an output means for outputting information relating to the searched common points for each cluster.

[0010] The labeling support method according to the present invention is characterized in that a computer generates multiple clusters by classifying data to be labeled using unsupervised learning, the computer searches for common points in the data contained in each generated cluster, and the computer outputs information regarding the common points searched for for each cluster.

[0011] The labeling support program according to the present invention is characterized in that it causes a computer to execute a classification process that generates multiple clusters by classifying data to be labeled using unsupervised learning, a search process that searches for common points among the data contained in each generated cluster, and an output process that outputs information about the common points found for each cluster. [Effects of the Invention]

[0012] According to the present invention, it is possible to assist in labeling clusters into which unlabeled data has been classified. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a block diagram showing an example of the configuration of an embodiment of a labeling support system according to the present invention. [Figure 2] FIG. 1 is an explanatory diagram showing an example of data used in the labeling support system. [Figure 3] FIG. 10 is an explanatory diagram illustrating an example of feature amounts. [Figure 4] FIG. 10 is an explanatory diagram showing an example of graph visualization of dimension-reduced data. [Figure 5] FIG. 10 is an explanatory diagram showing an example of a graph displaying the contribution of each sensor. [Figure 6] FIG. 10 is an explanatory diagram showing an example of the distribution of sensor values ​​within a cluster. [Figure 7] FIG. 10 is an explanatory diagram showing an example of statistics within a cluster. [Figure 8] 10 is a flowchart showing an example of the operation of the labeling support system. [Figure 9] 1 is a block diagram showing an overview of a labeling support system according to the present invention. [Figure 10] FIG. 1 is a schematic block diagram illustrating the configuration of a computer according to at least one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the following description, moving images (video data) will be used as an example of unlabeled data. However, unlabeled data is not limited to moving images, and may be, for example, still images, music data, text data, etc. Furthermore, unlabeled data (data to be labeled) may be referred to as unclassified data hereinafter.

[0015] 1 is a block diagram showing an example of the configuration of an embodiment of a labeling support system according to the present invention. The labeling support system 1 of this embodiment includes a data acquisition unit 10, a related information acquisition unit 20, an object identification unit 30, a data processing unit 40, a text information input unit 50, a feature extraction unit 60, a feature storage unit 70, a visualization processing unit 80, and an input / output device 90.

[0016] The data acquiring unit 10 acquires data to be labeled (i.e., unclassified data). For example, when a moving vehicle is captured by a camera (not shown), the data acquiring unit 10 may acquire a video of the vehicle captured by the camera as data to be labeled. Note that the data acquired by the data acquiring unit 10 is not limited to data acquired in real time. For example, the data acquiring unit 10 may acquire the data to be labeled from a storage server (not shown) in which the data to be labeled is stored.

[0017] The related information acquisition unit 20 acquires information related to the data to be labeled (hereinafter referred to as related information). In this embodiment, the related information is information indicating the circumstances under which the data to be labeled was generated, such as information indicating the place (place where the image was taken) and time when the data was generated, and data acquired by a sensor (hereinafter referred to as sensor data).

[0018] For example, if the data to be labeled is video data captured by an in-vehicle camera (drive recorder), relevant information may include GPS (Global Positioning System) information indicating the vehicle position, information acquired based on a CAN (Controller Area Network), etc. Examples of sensor data acquired in this case include speed, acceleration, and position (latitude, longitude, altitude, etc.).

[0019] Furthermore, when video showing the operating status of a thermal power plant is used as the data to be labeled, examples of sensor data include fuel flow rate, pressure, temperature, rotation speed, power generation, etc. In addition, when video showing the status of a farm is used as the data to be labeled, examples of sensor data include time, temperature, humidity, pH, soil moisture content, solar radiation, wind direction and speed, water level, etc.

[0020] The object identification unit 30 identifies objects included in the acquired data and generates information specifying the identified objects (hereinafter referred to as an object list). For example, if the object to be identified is a vehicle, the object identification unit 30 may identify the vehicle from the data acquired by the data acquisition unit 10 and generate information specifying the vehicle (for example, coordinates indicating its position in the image) as an object list. Note that methods for identifying objects from images or videos are widely known, and detailed explanations will be omitted here.

[0021] The data processing unit 40 processes the data (more specifically, the object list) into a format that can be used when the feature extraction unit 60, which will be described later, performs processing. Specifically, the data processing unit 40 processes the data so as to improve the accuracy of feature extraction and clustering. The data processing unit 40 performs, for example, data thinning, missing value interpolation, outlier removal, and deletion of unnecessary data items. Furthermore, for example, if the data to be labeled is video data, the data processing unit 40 may convert the video data into numerical time-series data.

[0022] The text information input unit 50 accepts input of text data including information to be added to each piece of data to be labeled (hereinafter referred to as additional information). Additional information is information indicating the content of the data to be labeled that can be obtained other than related information. Examples of categories indicating additional information include weather, plant types, and traffic participants. Examples of weather category values ​​include sunny, cloudy, rainy, and snowy. Examples of plant type category values ​​include rice, wheat, and barley. Examples of traffic participants include automobiles, bicycles, and pedestrians.

[0023] Note that input of text data is optional. That is, additional information does not need to be input for the data to be labeled. However, it is preferable to input additional information because the more additional information there is for the data to be labeled, the more accurate the classification can be. In the following description, data to be labeled that is associated with additional information will also be simply referred to as data to be labeled.

[0024] FIG. 2 is an explanatory diagram showing an example of data used in the labeling support system 1 of this embodiment. The example shown in FIG. 2 shows that the data acquisition unit 10 acquires video 11 as data to be labeled, and the related information acquisition unit 20 acquires related information 21 related to the location where the video 11 was taken, etc. The example shown in FIG. 2 also shows that the data processing unit 40 processes the video 11 and related information 21 (more specifically, the object list generated by the object identification unit 30) to generate numerical time-series data 41. Furthermore, the example shown in FIG. 2 shows that the text information input unit 50 has received input of text data 51 including information related to weather, scene, time period, and object as additional information.

[0025] The feature extraction unit 60 extracts features from each piece of data to be labeled. The feature extraction unit 60 of this embodiment generates multiple clusters by automatically classifying each piece of data to be labeled, including additional information, through unsupervised learning. Any method for generating clusters through unsupervised learning may be used, and examples include the k-means method and a Gaussian mixture model.

[0026] Then, the feature extraction unit 60 extracts a feature amount of each data included in the generated cluster. For example, the feature extraction unit 60 may extract additional information included in text data as a feature amount. Alternatively, the feature extraction unit 60 may extract a feature amount indicated by the numerical time series data. Specifically, the feature extraction unit 60 may extract a feature amount based on a sensor value included in the data to be labeled (more specifically, the numerical time series data).

[0027] The method for extracting features from the numerical time series data is arbitrary. For example, for each cluster generated by the k-means method, the feature extraction unit 60 may extract a feature called the distance from the center of gravity of the numerical time series data included in the cluster to each data (cluster distance feature).

[0028] In this way, the feature extraction unit 60 can also be called a classification means, since it performs a process of classifying data to be labeled by unsupervised learning. Furthermore, in this embodiment, the object identification unit 30 identifies an object from information acquired by the data acquisition unit 10 and the related information acquisition unit 20, and the data processing unit 40 processes the data of the identification result into a format used by the feature extraction unit 60. However, the data acquisition unit 10 may directly acquire data in a format used by the feature extraction unit 60 and input the acquired data to the feature extraction unit 60. In this case, the labeling support system 1 does not need to include the related information acquisition unit 20, the object identification unit 30, and the data processing unit 40.

[0029] The feature storage unit 70 stores the feature amounts of each data item extracted by the feature extraction unit 60. The feature storage unit 70 may store the feature amounts for each data item in any manner. FIG. 3 is an explanatory diagram showing an example of feature amounts stored in the feature storage unit 70. In the example shown in FIG. 3, the vertical direction represents one feature point, and the horizontal direction represents the feature amounts (category values) of each category (e.g., weather, traffic participants, types of plants, etc.). The feature storage unit 70 is realized, for example, by a magnetic disk or the like.

[0030] The visualization processing unit 80 performs processing for visualizing information that contributes to the labeling work for the generated clusters. The visualization processing unit 80 includes a search unit 81 and an output unit .

[0031] For each generated cluster, the search unit 81 searches for commonalities among the data to be labeled that are included in that cluster. Specifically, the search unit 81 extracts feature amounts of each data item included in the generated cluster and searches for commonalities among the feature amounts of the extracted data items. The search unit 81 may search for commonalities among category values ​​in each extracted category as feature amounts, or may search for commonalities among feature amounts extracted based on numerical time-series data.

[0032] For example, when focusing on the above-described categories, the search unit 81 may determine that a category value is a common point if the proportion of data in a cluster that has a common category value exceeds a predetermined threshold. Specifically, the proportion can be calculated based on the ratio of the number of data items that include common points to the number of data items in the cluster. In this case, the search unit 81 may search for common points for category values ​​of all categories, or may search for common points for category values ​​of any part of categories.

[0033] Alternatively, in the process of searching for common points, the searching unit 81 may search for the most common category value (for example, the most frequent value in the case of numerical values) for each category indicated by the data to be labeled as the common point. Then, the searching unit 81 may identify the category value with the highest proportion of the most common category value as the common point.

[0034] Furthermore, for example, when a feature is extracted based on a sensor value indicated by the numerical time-series data, the search unit 81 may calculate the contribution of the sensor value to the feature. For example, when the relationship between the sensor value of the data to be labeled and the feature is expressed by a linear expression of the sensor value, the search unit 81 may determine the weight of the sensor value included in the linear expression as the contribution, and identify the sensor value with the largest weight as the common point.

[0035] The output unit 82 outputs information related to the common points found for each cluster. The output unit 82 may output the information related to the common points found for each cluster to the input / output device 90 for display, or may output the information to a storage unit (not shown) included in the labeling support system 1 for storage.

[0036] Specifically, the output unit 82 may output one common point having the highest degree of commonality from among the common points found. For example, if a category value is identified as the common point, the output unit 82 may output the name of the category value and the category value (e.g., "Weather: Sunny"). Also, for example, if a sensor value is identified as the common point, the output unit 82 may output the sensor value and the name of the sensor that obtained the sensor value.

[0037] Furthermore, when the contribution of the sensor value to the feature amount has been calculated, the output section 82 may output the sensor value and the name of the sensor value with the largest contribution as a common point.

[0038] The output unit 82 may also output multiple common point candidates found within a cluster according to the degree of commonality of the common points. For example, the output unit 82 may output the degree of commonality itself, or may output common points with the highest degree of commonality as labeling candidates in a ranking format up to a predetermined order.

[0039] Furthermore, the output unit 82 may directly label and output information indicating the found common points for the unclassified data in each cluster (i.e., data to be labeled). In this case, the output unit 82 may label and output information indicating the common point with the highest degree of commonality.

[0040] Furthermore, the output unit 82 may visualize the dimension-reduced (lowered) data to be labeled by plotting a graph on the input / output device 90 so that a human can observe how the data to be labeled is clustered. The output unit 82 may reduce the dimension of the data to be labeled to two or three dimensions by, for example, UMAP (Uniform Manifold Approximation and Projection) or the like, and visualize the dimension-reduced data as a graph such as a distribution map. In this case, the output unit 82 may display data classified into the same cluster in a different manner (for example, by using a different color or symbol) from other clusters.

[0041] Fig. 4 is an explanatory diagram showing an example of dimensionally reduced data visualized in a graph. The graph shown in Fig. 4 shows an example of data that has been reduced to two dimensions using UMAP, displayed in different ways (diagonal lines, black boxes, etc.) for each cluster to which it belongs. As shown in Fig. 4, the output unit 82 may also display a circle around the range of data included in a cluster so that the range can be identified.

[0042] Furthermore, when drawing a graph, the output unit 82 may display all data, or may determine to display only data that satisfies a specific condition, or not to display any data at all. For example, the output unit 82 may determine whether to display clusters that satisfy a specific condition (e.g., clusters with a larger number of data points than a predetermined number) or unclassified data (i.e., unlabeled data).

[0043] Furthermore, when the contribution of the sensor values ​​to the feature quantities has been calculated, the output unit 82 may graphically display the contribution of each sensor within the cluster. FIG. 5 is an explanatory diagram showing an example of a graphical display of the contribution of each sensor. In the example shown in FIG. 5, the feature quantities of each cluster are calculated using sensor values ​​indicating temperature, humidity, and water level, and the contribution of each sensor value used to calculate the feature quantities is displayed as a bar graph. For example, the feature quantities of cluster 2 indicate that the sensor value indicating the water level has a higher contribution than the other clusters.

[0044] The display of the contribution rate for each sensor is not limited to the bar graph exemplified in FIG. 5, but may be, for example, a grouped vertical bar graph, a line graph, a three-dimensional surface graph, or the like.

[0045] Furthermore, the output unit 82 may output the distribution of sensor values ​​within a cluster. FIG. 6 is an explanatory diagram showing an example of the distribution of sensor values ​​within a cluster. In the example shown in FIG. 6, the data to be labeled includes temperature, humidity, and water level as sensor values, and a graph showing the distribution for each sensor value is displayed as shown in FIG. 6. Note that the vertical axis of the graph shown in FIG. 6 indicates the number of elements, and the horizontal axis indicates the sensor value. Note that the display of the distribution of sensor values ​​within a cluster is not limited to the distribution diagram shown in FIG. 6, and may be, for example, a frequency distribution table or a histogram.

[0046] Furthermore, the output unit 82 may output statistics within a cluster. Fig. 7 is an explanatory diagram showing an example of statistics within a cluster. The statistics illustrated in Fig. 7 show an example in which the average, variance, maximum value, and minimum value of each sensor value included in the data within the cluster are output for each cluster. Note that the output statistics are merely examples, and any other statistics, such as the median or mode, may be output.

[0047] The input / output device 90 displays the output results from the output unit 82. The input / output device 90 also receives input from the user regarding the displayed results and executes processing according to the input. For example, when receiving input from the user specifying a cluster, the input / output device 90 may display detailed information about the specified cluster. Specifically, the input / output device 90 may display statistical information generated by the output unit 82 for the specified cluster.

[0048] The input / output device 90 may be realized by a tablet terminal, etc. Alternatively, the input / output device 90 may be realized by a device having a display device and a pointing device, etc.

[0049] For example, when a range of clusters as illustrated in FIG. 4 is displayed, the input / output device 90 may receive input from the user specifying the target cluster, and display information about the received cluster (for example, information illustrated in FIGS. 5, 6, and 7).

[0050] The data acquisition unit 10, related information acquisition unit 20, object identification unit 30, data processing unit 40, text information input unit 50, feature extraction unit 60, and visualization processing unit 80 (more specifically, search unit 81 and output unit 82) are realized by a computer processor (e.g., a CPU (Central Processing Unit)) that operates according to a program (labeling support program).

[0051] For example, the program may be stored in a storage unit (not shown) of the labeling support system 1, and the processor may read the program and operate as the data acquisition unit 10, the related information acquisition unit 20, the object identification unit 30, the data processing unit 40, the text information input unit 50, the feature extraction unit 60, and the visualization processing unit 80 (more specifically, the search unit 81 and the output unit 82) in accordance with the program. Also, the functions of the labeling support system 1 may be provided in a SaaS (Software as a Service) format.

[0052] The data acquisition unit 10, the related information acquisition unit 20, the object identification unit 30, the data processing unit 40, the text information input unit 50, the feature extraction unit 60, and the visualization processing unit 80 (more specifically, the search unit 81 and the output unit 82) may each be realized by dedicated hardware. Furthermore, some or all of the components of each device may be realized by general-purpose or dedicated circuits, processors, etc., or a combination of these. These may be configured by a single chip, or by multiple chips connected via a bus. Some or all of the components of each device may be realized by a combination of the above-mentioned circuits, etc., and a program.

[0053] Furthermore, when some or all of the components of the labeling support system 1 are realized by a plurality of information processing devices, circuits, etc., the plurality of information processing devices, circuits, etc. may be centrally or decentralized. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, etc., in a form in which each device is connected via a communication network.

[0054] Next, the operation of the labeling support system 1 of this embodiment will be described. Fig. 8 is a flowchart showing an example of the operation of the labeling support system 1. The example of operation shown in Fig. 8 is an example of operation in which the data acquisition unit 10 directly acquires data in a format used by the feature extraction unit 60 and inputs the acquired data to the feature extraction unit 60.

[0055] The feature extraction unit 60 generates multiple clusters from the data to be labeled (step S51). The search unit 81 searches for common points in the data for each generated cluster (step S52). Then, the output unit 82 outputs information about the common points found for each cluster (step S53).

[0056] As described above, in this embodiment, the feature extraction unit 60 generates multiple clusters by classifying data to be labeled using unsupervised learning, and the search unit 81 searches for commonalities among the data included in each generated cluster. The output unit 82 then outputs information about the commonalities found for each cluster. This configuration can assist in the labeling process for clusters into which unlabeled data has been classified.

[0057] Furthermore, by having the output unit 82 automatically label the data to be labeled and outputting labeling candidates, the cost of manual labeling can be reduced and people can understand why the label was assigned.

[0058] Next, an overview of the present invention will be described. Fig. 9 is a block diagram showing an overview of a labeling support system according to the present invention. A labeling support system 190 (e.g., labeling support system 1) according to the present invention includes classification means 191 (e.g., feature extraction unit 60) that generates a plurality of clusters by classifying data to be labeled using unsupervised learning, search means 192 (e.g., feature extraction unit 60) that searches for common points among the data included in each generated cluster, and output means 193 (e.g., output unit 82) that outputs information about the common points searched for for each cluster.

[0059] Such a configuration can assist in labeling the unlabeled data into classified clusters.

[0060] Furthermore, the classification means 191 may extract the feature amount of each data included in the generated cluster, and the search means 192 may search for commonalities in the feature amount extracted for each data in the cluster.

[0061] In addition, the classification means 191 may extract features based on sensor values ​​included in the data to be labeled, the search means 192 may calculate the contribution of the sensor values ​​to the features, and the output means 193 may output the sensor value with the largest contribution as a common point.

[0062] Furthermore, the output means 193 may display a graph of the contribution of each sensor within the cluster.

[0063] Furthermore, the output means 193 may label the data to be labeled in each cluster with information indicating the found common points and output the label.

[0064] Furthermore, the output means 193 may output a plurality of common points found within a cluster in accordance with the degree of commonality of the common points.

[0065] Furthermore, the output means 193 may output common points with the highest degree of commonality as labeling candidates in a ranking format up to a predetermined rank.

[0066] 10 is a schematic block diagram showing the configuration of a computer according to at least one embodiment. The computer 1000 includes a processor 1001, a main memory device 1002, an auxiliary memory device 1003, and an interface 1004.

[0067] The above-described labeling support system 190 is implemented in a computer 1000. The operations of the above-described processing units are stored in the form of a program (labeling support program) in an auxiliary storage device 1003. The processor 1001 reads the program from the auxiliary storage device 1003, loads it into the main storage device 1002, and executes the above-described processing in accordance with the program.

[0068] In at least one embodiment, the auxiliary storage device 1003 is an example of a non-transitory tangible medium. Other examples of non-transitory tangible media include a magnetic disk, a magneto-optical disk, a CD-ROM (Compact Disc Read-only memory), a DVD-ROM (Read-only memory), and a semiconductor memory connected via the interface 1004. In addition, when this program is distributed to the computer 1000 via a communication line, the computer 1000 that receives the program may load the program into the main storage device 1002 and execute the above processing.

[0069] The program may also be a program for realizing part of the above-described functions. Furthermore, the program may be a so-called differential file (differential program) that realizes the above-described functions in combination with another program already stored in the auxiliary storage device 1003.

[0070] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0071] (Appendix 1) A classification means for generating multiple clusters by classifying data to be labeled using unsupervised learning; a search means for searching for commonalities among the data included in each of the generated clusters; and an output means for outputting information about the common points found for each cluster. A labeling support system characterized by:

[0072] (Appendix 2) The classification means extracts the features of each data included in the generated cluster, The search means searches for commonalities in the feature quantities extracted for each data in the cluster. Attachment 1: A labeling assistance system.

[0073] (Appendix 3) The classification means extracts features based on sensor values ​​included in the data to be labeled, The search means calculates the contribution of the sensor value to the feature amount, The output means outputs the sensor value with the largest contribution as a common point. 1. A labeling assistance system according to claim 1 or 2.

[0074] (Appendix 4) The output means displays a graph of the contribution of each sensor within the cluster. The labeling support system described in Appendix 3.

[0075] (Appendix 5) The output means labels and outputs information indicating the common points found for the data to be labeled in each cluster. 1. A labeling assistance system according to claim 1 or 2.

[0076] (Note 6) The output means outputs the commonalities found within the cluster according to the degree of commonality of the commonalities. 6. A labeling assistance system according to any one of appendices 1 to 5.

[0077] (Appendix 7) The output means outputs common points with the highest degree of commonality as labeling candidates in a ranking format up to a predetermined order. Attachment 6: A labeling assistance system.

[0078] (Appendix 8) A computer generates multiple clusters by classifying the data to be labeled using unsupervised learning, The computer searches for commonalities in the data included in each of the generated clusters; The computer outputs information about the common points found for each of the clusters. A labeling support method comprising:

[0079] (Appendix 9) The computer extracts the features of each data contained in the generated cluster, The computer searches for commonalities in the feature quantities extracted for each data in the cluster. The labeling assistance method described in Appendix 8.

[0080] (Appendix 10) To the computer, Classification process to generate multiple clusters by classifying the data to be labeled using unsupervised learning. a search process for searching for commonalities of the data included in each of the generated clusters; and An output process for outputting information about the common points found for each cluster. A program storage medium that stores a labeling assistance program for executing the above.

[0081] (Appendix 11) To the computer, In the classification process, the features of each data contained in the generated cluster are extracted, In the search process, commonalities of the extracted features for each data in the cluster are searched for. 11. A program storage medium according to claim 10, storing a labeling assistance program for:

[0082] (Appendix 12) To the computer, Classification process to generate multiple clusters by classifying the data to be labeled using unsupervised learning. a search process for searching for commonalities of the data included in each of the generated clusters; and An output process for outputting information about the common points found for each cluster. A labeling support program to help implement this.

[0083] (Appendix 13) To the computer, In the classification process, the features of each data contained in the generated cluster are extracted, In the search process, commonalities of the extracted features for each data in the cluster are searched for. Labeling assistance program as described in Appendix 12.

[0084] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Explanation of symbols]

[0085] 1. Labeling support system 10 Data Acquisition Section 20 Related Information Acquisition Department 30 Object Recognition Unit 40 Data Processing Department 50 Text information input section 60 Feature Extraction Unit 70 Feature Memory Unit 80 Visualization processing unit 81 Search Department 82 Output section 90 Input / Output Devices

Claims

1. a classification means for generating a plurality of clusters by classifying data to be labeled using unsupervised learning; a search means for searching for commonalities among the data included in each of the generated clusters; and an output means for outputting information about the common points found for each cluster. A labeling support system characterized by:

2. The classification means extracts features of each data included in the generated cluster, The search means searches for commonalities in the feature quantities extracted for each data in the cluster. The labeling support system according to claim 1 .

3. The classification means extracts features based on sensor values ​​included in the data to be labeled, The search means calculates the contribution of the sensor value to the feature amount, The output means outputs the sensor value with the largest contribution as a common point.

3. The labeling support system according to claim 1 or 2.

4. The output means displays a graph of the contribution of each sensor in the cluster. The labeling support system according to claim 3.

5. The output means labels the data to be labeled in each cluster with information indicating the common points found and outputs the label.

3. The labeling support system according to claim 1 or 2.

6. The output means outputs the common points found within the cluster in accordance with the degree of commonality of the common points. The labeling support system according to any one of claims 1 to 5.

7. The output means outputs common points with the highest degree of commonality as labeling candidates in a ranking format up to a predetermined rank. The labeling support system according to claim 6.

8. The computer generates multiple clusters by classifying the data to be labeled using unsupervised learning, The computer searches for commonalities in the data included in each of the generated clusters; The computer outputs information about the common points found for each of the clusters. A labeling support method comprising:

9. The computer extracts the features of each data contained in the generated cluster, The computer searches for commonalities in the feature quantities extracted for each data in the cluster. The labeling support method according to claim 8.

10. On the computer, Classification process to generate multiple clusters by classifying the data to be labeled using unsupervised learning. a search process for searching for commonalities of the data included in each of the generated clusters; and An output process for outputting information about the common points found for each cluster. A labeling support program to help implement this.

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