238 results about "Hierarchical cluster algorithm" patented technology

An automatic clustering method using an Average-linkage algorithm and a KPower Means algorithm, and a method and apparatus for multi-path clustering required for a spatial channel modeling (SCM) in a wireless communication environment are provided. The automatic clustering method, including: a first step of obtaining an initial cluster centroid using a hierarchical clustering algorithm; a second step of moving the initial cluster centroid using a two dimensional clustering algorithm; a third step of clustering a data set according to the moved initial cluster centroid; and a fourth step of calculating a validation index with respect to the clustered data set and determining an optimal number of clusters.

A model selection method is provided for choosing the number of clusters, or more generally the parameters of a clustering algorithm. The algorithm is based on comparing the similarity between pairs of clustering runs on sub-samples or other perturbations of the data. High pairwise similarities show that the clustering represents a stable pattern in the data. The method is applicable to any clustering algorithm, and can also detect lack of structure. We show results on artificial and real data using a hierarchical clustering algorithm.

The invention discloses an ROS (Robot Operating System) based robot automatic following method. According to the method, data is acquired by adopting a laser radar, preprocessing is performed on the data, the data is clustered by using a hierarchical clustering algorithm, a pedestrian double-leg model is taken as a pedestrian recognition feature, the position between the legs represents the pedestrian position, and a defect that the laser radar is not obvious in feature and low in recognition rate is solved by a method of resampling. The automatic following method is implemented by reasonably utilizing an ROS, message transfer and function implementation between the parts are facilitated, and a navigation framework of the ROS is utilized to enable a robot to have a certain navigation obstacle avoidance ability in the automatic following process.

The invention relates to a method and a system for discovering a user residence position based on mobile phone signaling. The method comprises the following steps: the obtained mobile phone signalingdata of the user is taken as the original trajectory data, and the original trajectory data is sequentially processed by data cleaning, data slicing, preliminary clustering, data verification and corecluster calculation, and finally the user staying position is obtained. Adopting a series of algorithms such as track slice aggregation, hierarchical clustering algorithm, kernel density clustering algorithm and machine learning algorithm, etc., considering mobile phone track point density, signaling time interval, track point moving direction and moving distance, etc., we can judge user 's staying position, staying time, entering and leaving staying point time. The algorithm can effectively eliminate the interference of base station drift on user location judgment, improve the identificationaccuracy of user dwell point position and dwell time, and objectively restore the user dwell position.

The invention discloses a Chinese Web document online clustering method based on common substrings. As known to all, search engines are important in application of information searching and positioning with sharp increase of information on the internet. Web document clustering can automatically classify return results of the search engines according to different themes so as to assist users to reduce query range and fast position needed information. The Web document online clustering is characterized in that non-numerical and non-structured characteristics of Web documents are required to be met on the one hand, and clustering time is required to meet online search requirements of users on the other hand. According to the two characteristics, the invention provides the Chinese Web document online clustering method based on common substrings, and the method comprises steps as follows: (1) firstly, preprocessing the first n query results returned by the search engines so as to realize deleting and replacing operation of non-Chinese characters in the return results of the search engines, (2) extracting common substrings in the Web documents by utilizing GSA, (3) presenting a weighting calculation formula referring to TF*IDF according to the common substrings which are extracted and then building a document characteristic vector model, (4) computing pairwise similarity of the Web documents on the basis of the model to acquire a similarity matrix, (5) adopting an improved hierarchical clustering algorithm to achieve clustering of the Web documents on the basis of the matrix, and (6) executing clustering description and label extraction. The Chinese Web document online clustering method based on common substrings has obvious advantages on performance, clustering label generation and clustering time effects.

The invention relates to the communication field, and provides a cluster communication terminal track real time anomaly detection method and system based on hybrid grid hierarchical clustering. The method comprises the steps: the step 1: constructing a track based on grids, and determining the size of the optimal grid; the step 2: calculating a Hausdroff distance matrix, utilizing a Hausdroff distance formula to calculate the distance between all the tracks based on the tracks of the grids, and generating a distance matrix of a track set; the step 3: hierarchical clustering, that is, based on the Hausdroff distance matrix of the track set, applying an agglomerate hierarchical clustering algorithm from bottom to top to realize classification of normal and abnormal tracks of a large scale of tracks; and the step 4: anomaly detection method evaluation and feedback: utilizing the above method to perform anomaly track detection on the track set which has a track classification identifier to obtain an anomaly classification result, and evaluating whether a model parameter is reasonable after comparison and making a feedback. The cluster communication terminal track real time anomaly detection method based on hybrid grid hierarchical clustering can realize on-line real time detection of an anomalous event, and can improve the upper layer dispatching efficiency of a cluster communication system.

The invention relates to an audio editing system. The audio editing system comprises a plurality of initial segmentation devices, a multi-sound track fusion device, an audio clustering device and a re-segmentation device, wherein the plurality of the initial segmentation devices are respectively used for initially segmenting audio streams from a plurality of sound tracks into a plurality of different paragraphs; the multi-sound track fusion device is used for integrating segmentation points of the plurality of the initial segmentation devices, selecting the audio stream of the optimal sound track between every two adjacent segmentation points, further getting a plurality of initially segmented fragments and fusing the plurality of the obtained initially segmented fragments into an uniform audio data file; the audio clustering device is used for performing clustering on the plurality of the initially segmented fragments under supervision based on a hierarchical clustering algorithm and clustering the initially segmented fragments belonging to the same nature to a category; and the re-segmentation device is used for training according to the clustering result of the audio clustering device to get a hidden Markov model corresponding to each type and performing Viterbi alignment segmentation on the uniform audio file to get the audio stream after re-segmentation. The accuracy in final speaker clustering can be improved through a high-precision speaker segmentation system.

The invention discloses a network abnormal flow detection method. According to the method, the traffic data of the target equipment can be collected, the traffic rule characteristics of each session message in the traffic data are extracted, the session messages are clustered by adopting the K value clustering algorithm in combination with the hierarchical clustering algorithm, the reliability ofthe clustering process is improved, and finally, the abnormal session messages in the traffic data are detected according to the clustering result. The purpose of automatically detecting the network abnormal flow is achieved, and the safety of network equipment is improved. In addition, the invention further provides a network abnormal flow detection device and equipment and a readable storage medium, and the technical effect of the network abnormal flow detection device and equipment corresponds to the technical effect of the method.

The invention relates to a practical method for removing artifacts from online electroencephalograph, and belongs to the technical field of biomedical information processing. The method corrects a down sampling of the reduced channel real-time electroencephalograph signals, a power frequency notch wave and a linear drift, the discrete wavelet transform is used for decomposing the down sampling of the reduced channel electroencephalograph signals into 7 layers, and single channel electroencephalograph signals are converted to multiple channels. The wavelet coefficients are reconstructed and used as inputs to ICA. Fast acquisition of independent components is implemented using Fast ICA algorithm. According to the characteristics of time domain, frequency domain and ordinal correlation of each artifact in the independent component, which is different from the normal electroencephalograph component, hierarchical clustering algorithm is introduced to cluster each independent component, the categories of artifacts are automatically recognized, the artifacts are reconstructed after the zero artifact is zero, and the reconstructed electroencephalograph signals are obtained. The method solves the problem that the prior method cannot automatically identify and remove a variety of conventional electroencephalograph artifacts in the absence of reduced channel.

A computer-implemented method for clustering a plurality of observations included in a dataset. The method includes selecting a subset of variables from a set of variables, where each variable in the subset is associated with each observation in the dataset; for each of a first number of times, randomly selecting an initial value for each variable in the subset to generate a seed value set that includes an initial value for each variable in the subset; generating a number of pre-clusters equal to the first number based on a similarity between each observation in the dataset and each of the first number of seed value sets; and applying a hierarchical clustering algorithm to the first number of pre-clusters to derive a second number of clusters.

The invention discloses a semantic similarity-based Java application program interface use mode recommendation method, which comprises the following steps of extracting the annotation information, anapplication program interface calling sequence and a method signature in a Java file in a project to form a metadata structure; using a hierarchical clustering algorithm for the metadata structure, and extracting an application program interface use mode; and based on the semantic similarity, carrying out the application program interface use mode recommendation. According to the method, the Javaapplication program interface use mode is recommended through the semantic similarity, the recommendation accuracy of the Java application program interface use mode is improved, the programming timeof developers is shortened, and the development efficiency of the developers is improved.

The invention discloses a zinc floatation state dividing method based on isomerism textural features. Zinc floatation image textural features are extracted by combining a gray-level co-occurrence matrix algorithm which has a good effect on high-frequency band textural features and a Gauss Markov random field algorithm which has a good modeling effect on low-and-medium-frequency texture images, and the zinc floatation image textural features are subjected to Gauss normalization to serve as a textural feature vector. In an integrated clustering algorithm, partitional clustering with high efficiency is conducted firstly to eliminate the influences of noise points and outliers, then a hierarchical clustering algorithm with high clustering quality and high stability is adopted to combine clustering centers output through partitional clustering, and then a final clustering result is obtained. Experiments prove that the extracted textural feature quantity has high mode separability, and foam in different states can be distinguished with the integrated clustering algorithm; furthermore, the method can be directly realized on a computer and is low in cost, high in efficiency and easy to implement.

The invention discloses a cross-project defect prediction method based on data screening and data oversampling. Reasonable data screening and data imbalance processing strategies are designed, and cross-project historical software module data truly similar to the project module data is screened by means of a hierarchical clustering algorithm, so that a cross-project software defect prediction model is protected from the influence of irrelevant cross-project historical software module data; then by means of an oversampling method, defective software module data is added, and a new dataset with relatively balanced classification is obtained, so that the cross-project software defect prediction model is protected from the influence of an imbalanced training dataset. According to the technical scheme, the method has the advantages of being simple and efficient, and the performance of the cross-project software defect prediction model can be well improved.

The invention discloses an ETC data-based highway service area driving-in traffic flow estimation method. The method comprises the following steps of: judging whether a vehicle enters a service area or not by utilizing a hierarchical clustering algorithm according to a road section where the service area is located and historical driving data of the vehicle, and calculating a shunting coefficientof the vehicle entering the service area; and obtaining the traffic flow entering the service area according to the shunting coefficient and the entering flow of an ETC portal and a toll station whichare located at the upstream of the service area in a certain time window. Under the condition that no traffic detection device is arranged at the entrance of the expressway service area, the historical ETC portal data information is utilized, the entrance flow of the service area is estimated according to the passage information of vehicles at the ETC portal. The method can be suitable for estimating the entrance flow of the expressway service area.

The invention provides an uplink channel estimation method of a large-scale MIMO system, and the method comprises the following steps: (1) modelling for a probability model of a channel through usinga Gaussian mixture model; (2) performing channel estimation through using optimal Bayesian parameter estimation; (3) giving an iterative initial value through using a hierarchical clustering algorithm; (4) solving an edge probability density function in the step (2) through using an approximate message passing algorithm; (5) iteratively solving parameters of the Gaussian mixture model through using an expectation maximization algorithm. In the method, sparse characteristics of channel gain in a beam domain are used fully, the Bayesian parameter estimation method is used, learning statistical information in advance is not needed, and compared with the conventional channel estimation based on LS, better MSE performance can be obtained.

A method for clustering large-capacity text includes presenting vector space of text, calculating similarity between each two texts, embedding pattern into dimensional space and using K-means algorithm to cluster texts to be two types, carrying out successive bisect till requirement is satisfied and pattern is not divided any more.

The invention discloses a semantic-based multi-keyword sorting search privacy protection system and method, and relates to three entities: a cloud data owner, a cloud data user and a public cloud server. The cloud data owner performs grammar and lexical analysis on plaintext documents in a client to generate a keyword set, calculates domain weighted scores and correlation scores of keywords, generates plaintext document vectors, establishes document indexes through a hierarchical clustering algorithm, and reserves a relationship among the plaintext documents. The cloud data user performs semantic extension on input search keywords to generate a semantically extended word set, and performs vectorization and encryption to generate trap door query vectors. The public cloud server matches thetrap door query vectors with cluster center vectors. Therefore, the trap door query vectors are prevented from being matched with a large amount of ciphertext document vectors; and the retrieval efficiency is greatly improved.

The invention discloses a zone collaborative optimization policy-based order processing method for an automatic sorting system. Collaborative optimization and data mining technologies are introduced for performing effective analysis and management on orders of the automatic sorting system. Firstly, a mathematic analysis model for processing the orders of the sorting system is subjected to zone collaborative optimization, and characteristics of the sorting system are analyzed; secondly, a zone collaborative optimization problem of the sorting machine is converted into a distribution seeking problem of a parallel machine; thirdly an item distribution sub-problem is established and a hierarchical clustering algorithm is designed for performing clustering processing; and finally an optimal order sequence is determined based on a Markov method. Therefore, the order sorting processing capability is improved and the problem of low logistics distribution efficiency in China is solved.

The invention provides a textile defect detection method based on hierarchical clustering and Gabor filtering. A hierarchical clustering algorithm is combined with a Gabor filter set; digital image pixel gray-level information of a flat textile surface based on an illumination light source is analyzed to automatically position surface defects of a textile; the textile defect detection method comprises the main three steps of dividing a grid pattern, extracting characteristics and comparing the characteristics. The textile defect detection method provided by the invention is especially suitable for automatic recognition of textile surface defects in digital images, acquired under the stable illumination light source, on the flat surface of the textile; the textile defect detection method is a method for automatically dividing the grid pattern from the textile image based on the hierarchical clustering algorithm and carrying out characteristic extraction and defect recognition on the grid pattern based on the Gabor filter set.

The invention discloses a test-case selection method based on user sessions and a hierarchical clustering algorithm. The method includes the following steps: acquiring server access logs, and carryingout sorting according to time; carrying out preprocessing and clustering to form a user session sequence set; calculating similarity distances among all user session sequences through using an improved user-session-sequence comparison algorithm; employing the improved condensing hierarchical clustering algorithm to cluster the user session sequences, and outputting final clustering results of test cases; and optimizing selection of the test cases through deleting redundant test cases. According to the method of the invention, representative user operation sequences can be quickly mined from the large number of server access logs to use the same as test cases, automation of test-case generation and optimization of test-case selection are realized, and subsequent work of automated functiontests of a server, performance tests, user behavior analysis and the like is facilitated.

The invention relates to a hierarchical clustering and deep learning-based container number identification method. The method comprises the following identification steps of 1, firstly obtaining candidate character regions through a single character detection algorithm; 2, through character filtering and character combination, obtaining real container number characters; 3, obtaining candidate textlines through a hierarchical clustering algorithm according to the container number characters; 4, through text line filtering, obtaining the final container number text line; and 5, performing identification on single characters in the text line by adopting a deep learning technology to finally obtain a container number identification result.

The invention discloses a Chinese microblog topic information processing method, and relates to reason analysis algorithms for emotional distribution of microblog events. The invention aims to solve the problems that a hierarchical clustering algorithm and a correction algorithm adopted in an existing microblog topic information processing method are low in accuracy and incapable of including event-related microblogs in correct topics. According to the Chinese microblog topic information processing method, event topics and related microblogs are mined with a hierarchical clustering ordering method of unsupervised learning and a microblog topic correction algorithm of semi-supervised learning, so that the purpose of performing emotional distribution statistics and analysis on the related microblogs is finally achieved. The Chinese microblog topic information processing method can perform microblog topic information processing more accurately. The present invention is applied to the microblog topic information processing field.The Chinese microblog topic information processing method is applied to the field of microblog topic information processing.

The present invention relates to a microblog big data interest community analysis optimization method based on a user experience. The method comprises a step of carrying out weighted reconfiguration of an original microblog network, a step of completing the community division of the reconfigured weighted network based on the discovery algorithm of a link community, and a step of using a hierarchical clustering algorithm to continuously merge two communities with a largest similarity, finally forming a link community through division, and generating a tree-shaped hierarchical diagram. Starting from aspects of interest modeling and community discovery, through analysing microblog content and a user behavior, a user is helped to find interested users and topics of the user. Compared with a traditional method, the accuracy, recall ratio and F value of the method of the invention are improved significantly.

The invention provides an object software oriented automatic refactoring method and relates to the technical field of software quality improvement. According to the method, a to-be-refactored software system is established as a class level multilayer dependency directed network model; refactoring preprocessing is carried out; class level network connecting components are combined; each class level network connecting component is converted into an entity set of the same class; semantic and structure coupling relationships among the entity set elements are analyzed; a method level coupling undirected network model is established; weight coefficients of different classes of coupling relationships among the nodes of the undirected network are determined; community division is carried out on each method level network; refactoring suggestions are generated; and the to-be-refactored software system is refactored. According to the method, starting from the angles of global cohesion and coupling of the whole software system, through combination of a semantic similarity, a structure similarity and a hierarchical clustering algorithm, a move function, a move attribute and extraction class refactoring operation suggestions are generated at the same time, and the intelligibility, reusability and maintainability of the code are effectively improved.

The invention relates to the field of power systems and the automation of the power systems, in particular to a method for single-phase earth fault line selection of a small current grounding system. According to the method, the zero sequence circuit of a single-phase earth fault is analyzed, the frequency characteristic of the transient state characteristic quantity of the fault is obtained, an optimal FIR filter is designed according to the frequency characteristic to extract the components of transient state capacitive zero sequence currents with obvious fault characteristics, and the interference of noise and the interference of the unbalanced operation condition of a power grid are eliminated. A hierarchical clustering algorithm is used, the polarity and the amplitude characteristics of the transient state capacitive zero sequence currents of the initial ends of all feed lines are integrated to carry out classification on the feed lines, and a single feed line with the change trend of the transient state capacitive zero sequence current easier than the transient state capacitive zero sequence currents of other feed lines is selected as a fault line. The method improves the reliability of the line selection, lowers signal sampling frequency needs, avoids multi-layer signal decomposition, saves computing resources, and is easy to achieve.

The invention discloses a multi-dimensional distance clustering anomaly detection method and system based on a time sequence, and belongs to the technical field of aviation safety. The multi-dimensional distance clustering anomaly detection method based on the time sequence comprises the following steps: step 1, preprocessing a trajectory data set, the preprocessing comprising cleaning and re-integration; 2, calculating the multi-dimensional similarity between the tracks; 3, for the multi-dimensional Hausdorff distance, constructing an inter-track similarity matrix; 4, carrying out a hierarchical clustering algorithm of the multi-dimensional hausdorff distance; selecting a hierarchical clustering algorithm in machine learning to perform hierarchical clustering based on the similarity matrix; and step 5, detecting the anomaly detection effect of the algorithm, constructing a track with anomalies in speed, direction, longitude and latitude, clustering the abnormal track with a normal track through the hierarchical clustering algorithm, and evaluating the clustering algorithm by selecting a correct rate, a precision rate, a recall rate and an F1 value.

The invention discloses a batching and stock layout iterative optimization method based on blanking utilization rate prediction, and the method comprises the following steps: order batching: carryingout the order batching optimization according to an order agglomeration hierarchical clustering algorithm meeting delivery date deviation and production constraints, and obtaining a plurality of feasible batching schemes; feature extraction and dimension reduction: taking the plates of the same material in each batch in each batch scheme as a minimum prediction sample; inputting the prediction model to obtain a prediction result; inputting all minimum prediction samples in each batch scheme into the blanking utilization rate prediction model to predict a layout result, and calculating layout prediction results of all materials in all batches in the batch scheme; calculating and evaluating a layout prediction result; calling a layout optimization algorithm for calculation; judging and outputting a result. The invention aims to provide the batching and stock layout iterative optimization method based on blanking utilization rate prediction, the utilization rate of raw materials can be greatly improved, the cost of the raw materials can be reduced, and the calculation time can be greatly shortened.

The invention discloses a webpage text extracting method based on text tag feature mining. The webpage text extracting method comprises the following steps: S1, preprocessing webpage tags and repairing Html tags; S2, selecting and extracting Html tag features; S3, clustering and mining tag features and selecting a text cluster; S4, adjusting tags in the text cluster empirically; S5, extracting a tag text of the text cluster. In the webpage text extracting method, tags of webpage source codes are mined, the webpage tags are clustered by a hierarchical clustering algorithm, a cluster in which the text tag is positioned is extracted, the tags in the tag cluster is adjusted according to experience, and text is extracted according to the adjusted text cluster feature. Compared with other news webpage text extracting methods, the webpage text extracting method has the characteristics of higher universality, higher accuracy, easiness in use and no need of special settings for specific webpages.

The invention discloses a hierarchical clustering-based suspicious taxpayer detection method. The hierarchical clustering-based suspicious taxpayer detection method includes steps of 1) according to interest relationship between the taxpayers, performing clustering based on a hierarchical clustering algorithm, and according to association degrees between the taxpayers, dividing the taxpayers into clusters which are interest associated societies; 2) with analysis of marked taxpayer sample data, extracting distinction features between the normal taxpayers and the abnormal taxpayers based on complex network indicators, such as degree distribution and clustering coefficient; and 3) calculating feature similarity degree between identified interest associated societies and the normal taxpayers, as well as between the identified interest associated societies and the abnormal taxpayers, and further detecting the suspicious taxpayers.

The present invention discloses a dynamic social network community structure evolution method based on incremental clustering to solve the problems of community structure detection and communication evolution tracking in a large scale network. The method comprises a step of extracting the core node of a whole network to form a core sub graph, a step of running a hierarchical clustering algorithm on the core sub graph at a time t=0 to obtain the initial structure of a core community, and using an extended algorithm on the above basis to obtain the community structure of the whole network, and a step of using an incremental clustering algorithm to obtain the core community structure of the whole network at present time according to the dynamic evolution condition of an adjacent time network at a time t which is larger than 0 and extending the core community structure to obtain a whole community structure. Through introducing the core sub graph, the incremental calculation in the whole network is avoided, the processing speed is accelerated, and thus the method is suitable for the community discovery in the large scale network. In addition, through introducing the concept of a community structure shift, the large error of the community structure after long time evolution is avoided, and the accuracy of community evolution tracking is improved.