318 results about "Apriori algorithm" patented technology

The application discloses a method for fault analysis, comprising the following steps of: obtaining original alarm information of multiple fault alarm events; preprocessing the original alarm information to obtain standard alarm information; calculating the standard alarm information by using an Apriori algorithm, and obtaining a correlation degree between each fault alarm event; obtaining a corresponding correlation rule according to the correlation degree between the fault alarm events; and analyzing the target fault alarm events by using the correlation rule, and obtaining a fault analysisresult when the target fault alarm events are received. According to the method for the fault analysis, the fault alarm event can be effectively analyzed, so that the processing efficiency of the fault of the operation and maintenance is improved, and the reliability of the system is further enhanced. The application further discloses a device for fault analysis, equipment, and a computer readablestorage medium, which have the above beneficial effects.

The invention relates to a running track prediction method aiming at a specific vehicle potential group. The running track prediction method comprises the following steps: looking up the potential group vehicle of a certain specific vehicle by using the original traffic data when the specific vehicle is discovered; judging whether the potential group vehicle is the specific vehicle; increasing the risk coefficient of the vehicle when the potential group vehicle is not the specific vehicle; adding a license number of the vehicle into a specific vehicle list when the risk coefficient of the potential group vehicle exceeds a predetermined threshold value; predicting a running track of the vehicle if the license number of the vehicle is added into the specific vehicle list; performing mode extraction on a running record of the specific vehicle by using a class Apriori algorithm to generate a rule set R during track detection; judging whether a final passing barrier of the specific vehicle exists in the generated rule set R; and looking up a prediction path of the specific vehicle according to the corresponding rule in R if the barrier exists in the rule set R, and otherwise, predicting the running track of the specific vehicle by establishing a Bayesian network of the final passing barrier of the specific vehicle. The prediction result provided by the invention can provide effective technical support for the decision making of related departments and the safety guarantee of urban roads.

The invention discloses an identification method for application layer protocol characteristics, comprising the following processes: capturing a flow packet, selecting an identification mode, pretreating the flow packet, combining a characteristic set in a characteristic database to identify current network flow and displaying an identification result. The characteristic set in the characteristic database is updated timely by adopting a set of data mining AC algorithm based on an Apriori algorithm, thereby improving the accuracy of identifying the application layer protocol, overcoming various disadvantages of identifying network protocols by the traditional manual analysis method, promoting the informatization and intellectualization of application layer protocol analysis work for enterprises and companies, reducing labor source load for enterprises and companies and improving work efficiency and rate of progress.

Apriori algorithms are popular data mining techniques for extracting association rules from a body of data. The computational complexity of these algorithms is reduced by representing itemset information at cells of a hypercube. The cells encode associations between the items of each transaction. Direct computation of a cell as a lexicographic combination of items accelerates the computation of itemsets, and thereby improves the computational runtime complexity of the apriori algorithm that discovers association rules. Even faster computation is achieved by a user selected cardinality that limits the maximum size of the itemsets.

The invention relates to an EHV (Extra-High Voltage) power grid fault rule mining method based on a rough set association rule. According to the method, a distributed data mining idea is adopted, historical fault data of an EHV power grid is subjected to fault rule mining by using an association rule mining method based on a rough set theory, a distributed decision table is subjected to attribute reduction by using an attribute reduction algorithm based on an information entropy, and then, an Apriori algorithm in association rules is applied to the decision table, which is subjected to reduction, so as to carry out fault rule extraction. According to the method, the problem of inadaptability to large-data-volume historical fault databases of traditional data mining methods can be solved effectively, the complexity of rule extraction is lowered, and the method has the advantage of high fault rule mining efficiency.

The invention provides an association rule mining method of large-scale data, and the method comprises the following steps that (1) the input data is subjected to classified preprocessing based on similarity, so that records in the same category have high similarity; (2) the data in each category is mined based on Apriori algorithm to obtain frequent item sets of all categories; and (3) the frequent item sets of all the categories are merged, and association rules which correspond to the frequent item sets which are more than the minimum confidence coefficient are determined to be strong association rules. According to the association rule mining method of large-scale data, unnecessary candidate item sets with small association can be reduced, so that the association rule mining efficiency of all the data is improved, and better expandability is realized.

A frequent changing pattern extraction device (100) which extracts a frequent changing pattern from an ever-changing network structure includes: a conversion unit (12) which converts each of a plurality of graph sequences into an operator sequence by expressing changes, from a first graph included in the graph sequence to a second graph which is temporally adjacent to the first graph, using operators indicating operations necessary to transform the first graph into the second graph, each graph sequence including a plurality of graphs that show temporal changes in the graphs and each of the graphs including a vertex corresponding to a data piece and an edge corresponding to a link between data pieces; and an extraction unit (18) which extracts an operator subsequence that appears at least a predetermined number of times in the plurality of operator sequences corresponding to the plurality of graph sequences, based on the anti-monotonicity used in the Apriori algorithm.

The invention discloses a driver emotion real time identification method fusing facial expressions and voices, comprising: first, tracking a face in real time through kinect SDK to obtain driver facial expression and voice signals; preprocessing the driver facial expression and voice signals; training a feature extraction model based on unsupervised feature learning and sparse coding according to a given objective function, and after obtaining the model, and inputting preprocessed information into the model to obtain emotion features based on the facial expression and voice signals; extracting words according to speaking contents, creating a dictionary according to frequent words obtained through an Apriori algorithm, and obtaining emotion features based on texts through the dictionary; and finally cascading the emotion features based on the facial expression and voice signals and the emotion features based on texts, inputting feature vectors into an SVM(Support Vector Machine), and training an SVM classifier to obtain an SVM model. The finally obtained SVM model can identify driver emotions and have high robustness.

The invention provides a lightning activity data statistics method based on a modified Apriori algorithm. The method includes: 1, calculating weighted support and weighted confidence; 2, performing vertical bit vector format conversion; 3, generating frequent bipartite graphs; 4, mining candidate sets. Items are imparted with proper weights according to actual needs, and the original support and the original confidence are modified into weighted support and weighted confidence which are more practical. In addition, according to the algorithm, item information is stored in the bit vector vertical data format, storage space is saved, and I/O efficiency is improved; according to the modified algorithm, based on the top-down concept, longest frequent item sets meeting the support and confidence requirements are located through frequent bipartite digraphs, and all frequent items meeting the requirements are generated according to properties of the frequent time sets. Through the application of the algorithm, the efficiency of the Apriori algorithm is improved in terms of both space and time, and the algorithm better meets the actual needs.

The invention discloses a night haze image defogging method based on dark channel prior and color correction, comprising steps of inversing a nigh foggy image to obtain an inversion image, using a dark channel prior algorithm to perform defogging on the inversion image and then inversing the inversion image again to obtain a brightness enhancement image, combining with and utilizing a bright enhancement image to obtain a reference image through guiding filtering to obtain a weak halation image I1, utilizing the mean value and the variance corresponding to the RGB channel of the weak halation image I1 to obtain a color correction image I2, utilizing the inversion image of the reference image to perform halo removal on the color correction image I2 to obtain an image I3, performing color correction to obtain an image I4, performing fusion on the I2 and the I4 to obtain the image having no the halo and the color cast, using the dark channel prior based on the local neighborhood to perform de-fogging, and obtaining the output image through the guiding filtering. The invention not only increases the brightness, the contrast ratio, and the average gradient, but is excellent in aspects of highlighting the image details and reducing the image distortion.

The utility model relates to a discovering algorithm with clustered cycling associated rule, based on a differing sequence method of time series vector. Firstly, in view of the drawback of the current discovering algorithm with cycling associated rule on the problem of dividing a plurality of time domains, an algorithm called CMDSA is proposed. The algorithm selects a time series vector which comprises a item supporting degree as the data character in time area to cluster; meanwhile, the clustering number is controlled by a DB principle to reach the best clustering result, so that each time area under the cycling associated rule can be identified more accurately and more useful cycling associated rules can be found compared with the current algorithm. Aiming at the fact that all the current algorithm of cycling associated rule are based on the Apriori algorithm and the efficiency is low, an algorithm of CFP-tree based on Fp tree is proposed. The algorithm of CFP-tree adopts cycling tailoring technique based on the condition FP tree to enhance the algorithm efficiency. Thus, the adoption of the discovering algorithm with cycling associated rule of CFP-tree is far better than the prior algorithm based on Apriori in the time and space efficiency.

The invention discloses an IaaS (Infrastructure as a Service) cloud platform network fault positioning method and system based on log analysis. The IaaS cloud platform network fault positioning system comprises a fault injection module, a log acquisition and analysis module, a knowledge generation module and a fault detection and positioning module. Firstly, by injecting various typical network faults, various corresponding fault logs are formed; then aiming at various faults, log information related to network faults of each layer of physical resources, an operation system, a virtual machine, an OpenStack and the like is respectively acquired, and fault feature mining is carried out on the acquired network fault log information by using an Apriori algorithm; on such basis, according to a maximal frequent item set and parameters, such as a supporting degree, a confidence degree and the like, association rules and knowledge, which correspond to the specific network faults, are generated by utilizing a bayes formula; and finally, when a system has a network fault again, the network fault can be compared with the association rules of a knowledge base and analyzed according to an acquired fault log, so that the layer on which the network fault occurs is positioned.

The invention provides a method and a system for analyzing a fault root cause. The method comprises the following steps that: according to the time attribute of each piece of data in a dataset, carrying out sorting, and segmenting the dataset according to a preset time window to obtain multiple groups of sub-datasets; according to an Apriori algorithm, obtaining a frequent item set and an association rule in the dataset, wherein the frequent item set contains a certain quantity of data with a strong association relationship; and according to the time attributes of the data in the frequent itemset, carrying out sorting, matching the data which ranks higher with adjoint warning cause data pre-stored in a warning cause database in sequence, if the data which ranks higher is successfully matched with the adjoint warning cause data pre-stored in the warning cause database, removing the data which ranks higher from the dataset, and continuously carrying out comparison from a next data itemuntil the data which fails to be matched and ranks higher is taken as the root cause of the data of which the time sequence ranks at the bottom in the frequent item set. The method is suitable for thefull-dimension monitoring scene of an IT (Information Technology) system, the pressure of operation and maintenance personnel is released, and the quality requirements of the operation and maintenance personnel are lowered.

The invention discloses an underwater image enhancement method based on a foreground model. The method comprises the following steps that: improving a background light estimation method so as to effectively avoid the influences of underwater image overexposure, artificial light sources and the like; combining with the cognition of people for the underwater image, and utilizing a dark channel priori algorithm to remove background scattering and extract the foreground model; and combining with a white balance algorithm to put forward a color correction method suitable for the underwater image, and utilizing the attenuation characteristic of light in water to correct channel gains according to a relationship between channel attenuation coefficients so as to compensate color distortion caused by attenuation; and utilizing the channel gains to regulate a fogless image, and finally, obtaining the enhanced underwater image. By use of the method, the enhancement effect of an object part is clear and distinct, and a visual effect is better; image blurring is effectively removed, so that the definition of the enhanced underwater image is greatly improved, image details are better, the image enhancement of the background part is not affected on the basis of the color correction of the foreground model, the enhanced underwater image has more natural integral colors, and image brightness is within an acceptable range.

The invention provides an association rule mining system based on an improved Apriori algorithm. The association rule mining system comprises a data pretreatment module, a connection module, a pruning module, a frequent item statistics module and an association rule generation module, wherein the data pretreatment module interacts with a database, and takes charge of converting text data in the database into an integer format capable of carrying out bit operation; the connection module, the pruning module and the frequent item statistics module are used for forming concrete realization of the Apriori algorithm, and taking charge of regeneration of a frequent item set; and the association rule generation module interacts with the frequent item statistics module, and takes charge of converting frequent items generated by the frequent item statistics module into specific association rules. By virtue of a frequent item statistics method based on bit operation, the complexity of pruning operation is simplified; and the database scanning frequency is reduced, so that the association rule mining efficiency is improved; the consumption of system resources is reduced; the relatively efficient and convenient association rule mining business can be provided for enterprises and merchants; and the association rule mining system has the relatively great practical value.

The present invention proposes an improved Apriori algorithm based method for mining a database association rule. According to the method, a transaction database is converted into a relational matrix, the converted relational matrix is a sparse matrix, and the relational matrix is stored with an orthogonal link list. A generation process of a frequent item set is converted into an operation process of a single link list node set corresponding to items in the corresponding relational matrix. According to the method, a database only needs to be scanned once, so that the shortcomings that Apriori and a related algorithm therefor generate a large amount of candidate sets and need to scan the database for multiple times are overcome, and the time of frequently performing I/O operations is shortened; then, when a frequent 2-item set is generated and found, only an intersection operation of a node set needs to be performed, so that less time is consumed; and a single link list constructed by a generated frequent k-item set is recorded, so that a generation process of a frequent K+1-item set is simplified, and a complex pruning process of the Apriori algorithm is avoided.

The invention discloses a video image haze-removing processing method, which can meet a real-time haze-removing function, and meanwhile, can effectively eliminate flicker of video images obtained after haze removing. The method comprises the following specific steps: calculating atmosphere optical value of each frame image through a dark channel and a self-adaptive adjustment method; calculating initial transmissivity of a first frame image through a dark channel apriori algorithm; constructing an inter-frame transmissivity estimation and neighborhood space energy function, and calculating optimal transmissivity value of each region block; refining the initial transmissivity through a quick guide filtering algorithm; and obtaining the video images after haze removing through a haze-removing model.

The invention discloses a ship electric power station fault diagnosing method based on a knowledge petri network. The method includes the steps that (1) fault symptom sets of units of a ship power station are obtained and screened according to a ship power station fault Petri network model; (2) by means of an improved Apriori algorithm, strong association rule mining is carried out on the fault symptom sets and the fault units; (3) by means of man-machine conversation, a user inputs fault symptom characteristic quantity and confidence, a system carries out fault symptom identification through fuzzy reasoning by means of a strong association rule to determine the fault units; (4) the fault units serve as a root database, faulty Petri subnets are extracted from the Petri network model, fault reason diagnosis is carried out by means of a forward operation and backward inference method, and according to diagnosis results, fault reasons, fault route graphs and a corresponding fault maintenance method are provided. The ship electric power station fault diagnosing method can avoid false negatives of the fault reasons, generates a fault propagation path, and improves accuracy and efficiency of ship power station fault diagnosis.

The invention discloses a commodity recommendation method compatible with O2O applications in an internet plus tourism environment. The method can be realized through the following steps. First, a tourist chooses a scenic spot to be visited and accesses all available commodities there from the scenic spot inquiring data base. The weight of each commodity is then initialized by the consideration of their click conversion rate and recorded sales. Records of commodities the tourist has browsed, purchased, added to his favorite or disliked will also be checked so as to update the weights of all the commodities. A collaborative filtering recommendation algorithm will be adopted to further update the weights of the commodities. The commodities are then listed in piles according to their weights from high to low and a certain amount of commodities enjoying the highest weights are then chosen as recommended commodities in a temporary list. Then an Apriori algorithm, a basic algorithm of frequent set of items mining method, will be adopted for possible packages of commodities to be purchased. The temporary list with recommended commodities is divided into two halves where the recommended commodities can be all browsed from high weights to low weights and each commodity will be re-listed so that the commodities at the first top half of the temporary list are the final ones to be recommended to the tourist.

The invention relates to the technical field of network management and discloses an association rule mining method for an alarm event. Based on a branch screening optimization policy and an Apriori algorithm, the method comprises the steps of sequentially reading each event item in a database, and generating a support degree calculation support array corresponding to each event item; on the basis of the Apriori algorithm, executing the branch screening optimization policy, and generating a frequent item set; on the basis of the frequent item set and the support degree calculation support array, calculating the confidence of an association rule to obtain an effective association rule under the restraint of minimum confidence. By constructing the support degree calculation support array, the calculation of a support degree is simplified, the reading frequency of the database is greatly reduced, and the algorithm efficiency is improved; by constructing an adjacent dictionary chain table, a binomial frequent set which meets the requirements on the support degree can be dynamically found, and the execution basis of the branch screening optimization policy is provided; ineffective branches are dynamically deleted, the binomial frequent set is quickly generated, and the algorithm efficiency is improved.

The invention discloses an association rule algorithm based on an Apriori improved algorithm. First of all, a transaction database D is preprocessed, after data records are simplified, the data records are all read into a memory, in the process when candidate sets are generated through connecting and cutting frequent item sets, the process when the candidate sets are generated is improved, a candidate item set is directly generated, the database is scanned for calculating support after the candidate sets are obtained, and since the candidate sets and the transaction database D are ordered, when the candidate sets are respectively searched for in each transaction T, i.e., each record, once values greater than a candidate item are sought, search of the transaction is stopped. According to the invention, the improved Apriori algorithm is applied to a pharmacy management system, results indicate that the performance of the improved algorithm is obviously better than a conventional algorithm, the operation is concise, and actual demands are better satisfied.

The invention discloses a Hadoop-based user health data analysis method and system. The system comprises a personal basic health information management module, a personal body measurement data management module, a statistic analysis module, a data mining module and a distributed type storage module. A Hadoop platform is introduced on a service logic layer to process and analyze big data, an interface for HBase operation is provided on a data access layer, and efficient and persistent storage of data is achieved through HBase. When the health big data are processed, a correlation analysis data mining mode is mainly adopted, and the improved MapReduce-based Apriori algorithm is used. The Hadoop distributed processing platform is introduced on the service logic layer, and the processing speed of the health data is greatly improved. Correlation analysis is carried out through the MapReduce-based Apriori algorithm, and health advices and early warning can be provided for users in time. On the aspect of data storage, the HBase is adopted and suitable for the characteristics of diversification and rarefaction of the health data, the space needed by data storage is greatly reduced, and persistent storage of the big data is facilitated.

The invention discloses a power transformer defect data mining method. The method includes the following steps that: defect attribute screening is performed on the historical defect data set D0 of a power transformer, so that a defect data set D1 can be formed; filling or deletion is performed on defect attributes in the D1, so that noise data can be decreased; new attributes are constructed based on existing attributes of the D1, discretization is performed on continuously-valued attributes, reasonable stratification is performed on categorical attributes, and therefore, a defect data set D2 can be formed; the correlation between input attributes and target attributes is calculated, uncorrelated attributes are deleted, the remaining attributes form a defect data set D3; the association relationships between the attributes of the defect data set are calculated by using an Apriori algorithm; and effective association rules are extracted, the defect factors of the power transformer are analyzed, an association rule knowledge base can be formed. With the power transformer defect data mining method of the invention adopted, the defects of the power transformer can be mined in a multi-dimensional and multi-level manner, the association relationships between the attributes can be extracted conveniently and fast, a basis can be provided for power transformer condition evaluation, and the accuracy of condition evaluation can be improved.

The invention provides a strip-shaped time series data mining method based on data patterns. Data pre-processing is conducted on PDA monitoring data for cold-rolled sheet production, noise data, vacancy data and inconsistent data in original data are excluded; findings of a frequent item set and a plurality of item sets are conducted on data after being processed, and the frequent item set and the item sets are found out; association rule findings are conducted on the found frequency item set in the finding process of the frequency item set, and a potential association rules hidden in the data are found out. According to the method, because an average support degree and an average confidence coefficient threshold value are brought in and used in an Apriori algorithm, a significative frequency item set and association rules can be efficiently mined, insignificant data association can be effectively removed, a data basis can be provided for controlling of a high-precision strip shape, the adjustment time of the strip shape is greatly shortened, the control precision of the strip shape is improved, and the aggregative indicator of the strip shape is stably controlled to be within 5I.