56 results about "Affinity propagation clustering" patented technology

The invention discloses a holographic touch interactive exhibition system with multisource input and intelligent information optimizing functions. The system senses surrounding environment information by the aid of a plurality of sensors, a smoke sensor is started to monitor fire without audiences, faces in videos are recognized when the audiences are present, proper broadcasting information is recommended, and the identity sense of customers is enhanced. The exhibition system introduces an interactive mode, and the audiences can realize man-machine interaction by the aid of a holographic touch screen to acquire information that the audiences want to know. Besides, by the aid of an intelligent information mining algorithm and features extracted by a color co-occurrence matrix, finding of a facial expression recognition area of interest and feature extraction are combined into one step, the faces are clustered by the aid of an affinity propagation clustering algorithm, and the broadcasting information is changed according to emotion. By the aid of the exhibition system, the information can be effectively pushed according to ages and genders of the audiences in a complicated background environment, sufficient exhibition can be realized, the audiences can be guided to more comprehensively know the information, and the identity sense of the audiences is enhanced.

The invention discloses a method for microblog hot event online detection based on emotion analysis. According to the method for microblog hot event online detection based on emotion analysis, emotion extraction is conducted on online microblog texts according to emotion analysis, the bursting state of emotions and emotion texts is detected according to the improved Kleinberg algorithm, hot topics are extracted according to an affinity propagation clustering algorithm in the bursting period, so that only a small part of texts with a large amount of effective information participate in event extraction, the data volume is reduced, meanwhile a large amount of data noise is filtered out, and therefore the purpose that online real-time detection of the microblog hot events is achieved.

The invention discloses a method, equipment and a device for improving an intelligent customer service response rate, and a storage medium. The method comprises the following steps that: obtaining thenon-response questions of intelligent customer service, and classifying the non-response questions to obtain knowledge type questions; calculating a first similarity among knowledge type questions; according to the first similarity, through an affinity propagation clustering algorithm, clustering each knowledge type question to obtain a plurality of clustering clusters; taking the clustering center of each clustering cluster as a target standard question, and obtaining a target standard answer corresponding to the target standard question; and adding the target standard question and the corresponding target standard answer into an intelligent customer service knowledge base. In the method, the knowledge type questions in the non-response questions are clustered through the affinity propagation clustering algorithm, the clustering center of each obtained clustering cluster is taken as the target standard question to be added into the intelligent customer service, and the clustering center is a question with the highest representativeness in each clustering cluster so as to improve the intelligent customer service response rate and improve user experience.

The invention is suitable for the technical field of intrusion detection and provides an intrusion detection method and device. The intrusion detection method includes subjecting an intrusion detection data set KDD CUP99 to data standardizing to obtain a high-dimension vector set with n dimensions; subjecting the high-dimension vector set to dimension reduction to obtain a low-dimension vector set with m dimensions, wherein m is smaller than n; utilizing a semi-supervised affinity propagation clustering algorithm to process the low-dimension vector set to obtain a clustering result; determining a normal cluster and at least one abnormal cluster in the clustering result according to data size in obtained clusters; acquiring data of the abnormal clusters, wherein the data are abnormal data obtained by intrusion detection. By the intrusion detection method, probability distribution of data class is enabled to be close to original distribution obtained by using all attributes as much as possible, calculating speed of the subsequent clustering algorithm is increased greatly, and abnormality detection accuracy of an intrusion detection model is improved.

The invention discloses an extensible partition method for associated flow graph data, comprising the following steps: (1) preprocessing graph data and converting a node as the node ID; (2) adding each preprocessed side which is existed in an ID form in FIFO and waiting to enter into a sliding window to process; 3) completing the sliding window and calculating the PageRank value of each node according to a mixing approximation PageRank method; (4), tracking the collection of the initial node corresponded by each node in a subgraph, which is composed of sides in the sliding window and obtaining associated value of each node; (5) obtaining a plurality of centroids and corresponding clusters by adopting an affinity propagation clustering algorithm to all nodes in the window; (6) obtaining a plurality of partitioning results which are not of uniform size via associating repeated iteration of a clustering method and storing the partitioning results whose scales are less than threshold value in the sliding window; (7) transmitting the partitioning results whose scales reach or exceed the threshold value to the appropriate storage node by using a data distribution method; and finishing the graph data partitioning. Compared with the prior art, the method provided by the invention has higher partitioning quality.

The invention relates to an affinity propagation clustering-based integrated classifier constructing method. The method comprises the following steps: S1, preprocessing data; S2, obtaining a characteristic distance negative matrix; S3, clustering characteristics by utilizing an affinity propagation clustering algorithm; S4, constructing base classifiers; S5, repeating the step S4 till the quantity of the base classifiers is up to a preset value; S6, screening the base classifiers; and S7, integrating the base classifiers. The method disclosed by the invention has the advantages that the method can be matched with existing characteristic filtering methods and has a broader application prospect; genes are grouped by utilizing affinity propagation clustering through adopting bicor correlation coefficients as a relevance maxim and characteristic subspaces are constructed in a random selection way on this basis, so that better base classifiers with diversity can be obtained; the base classifiers are fused by utilizing a majority voting method; and therefore, by adopting the method disclosed by the invention, better classifying effect can be obtained and the classifying performance is stable at the same time.

The invention discloses a polarized SAR (synthetic aperture radar) image classification method based on improved affinity propagation clustering. The problem of low classification accuracy in the existing unsupervised polarized SAR classification method is mainly solved. The method comprises the implementation steps of: carrying out four component decompositions on each pixel point, extracting four scattering powers of each pixel point; dividing an image according to the obtained scattering powers to obtain four classes; equally dividing each obtained class into 20 small classes; clustering the 20 small classes in each class by the improved affinity propagation clustering to obtain the pre-classification result of the image; and finally, carrying out iterative classification on a pre-classified image by a Wishart classifier to obtain the final classification result. Compared with the classical classification method, for the method disclosed by the invention, the division on a polarized SAR image is stricter; the classification effect is better; the computation complexity is small; and the polarized SAR image classification method can be used for carrying out terrain classification and target identification on the polarized SAR image.

The invention discloses an image automatic marking method. The method comprises the following three steps of: selecting some representative images from a personal photo gallery through an affinity propagation clustering algorithm to describe cluster visual information, so that the training image number can be greatly reduced; reasonably marking the representative images according to the visual information and a semantic annotation consistency principle, so that the image searching and managing performance can be improved; and automatically marking the other images through a random walk algorithm with a restarting function.

The present invention relates to a sparse representation-based single-image rain elimination method. The method includes the following steps that: the low-frequency part and high-frequency part of an original image are obtained; non-overlapping partitioning is performed on the high-frequency part; online dictionary learning is carried out, so that a corresponding dictionary DHF composed of a plurality of sub-blocks is obtained; each of the sub-blocks of the DHF are divided into two categories by using an affinity propagation clustering method, wherein one category represents the geometric component information of the high-frequency part, and the other category represents the rain component information in the high-frequency part; the color distribution and edge direction of each sub-block are extracted, and each of the sub-blocks of the formed rain dictionary of the high-frequency part are classified again; a rain component in the high-frequency part is restored; the geometric component of the high-frequency part is obtained; and a rain-eliminated image can be obtained. The method of the invention has a better rain elimination effect.

The invention discloses an ensemble classification method based on randomized greedy feature selection, and belongs to the field of bioinformatics and data mining. The method is used for classifying gene expression data related to plant stress response. The method includes the following steps that 1, randomness is introduced into a traditional greedy algorithm to conduct feature selection; 2, a weighting local modular function serving as a community discovery evaluation index in a complex network is used as heuristic information of the randomized greedy algorithm; 3, base classifiers are trained in each feature subset with a support vector machine algorithm; 4, clustering partition is conducted on the base classifiers with an affinity propagation clustering algorithm; 5, base classifiers serving as class representative points in the cluster are used for conducting integration, and an ensemble classification model is formed with a simple majority voting method. By means of the method, whether plant samples are stressed or not can be recognized according to gene expression data, and the microarray data classification precision is greatly improved; besides, the algorithm is high in generalization capability and has very high stability.

The invention relates to a method for obtaining interval parameters of typical working conditions of harmonic users. The method comprises the following steps of obtaining maximum values and minimum values of a plurality of monitoring time segments of any phase in harmonic detection data of users to form an interval data sample set; performing interval principal component analysis on the interval data sample set by adopting CPCA, and selecting a sample characteristic quantity according to a variance contribution rate; calculating the similarity between data points for the sample characteristic quantity, and defining input parameters to perform affinity propagation clustering until an algorithm is subjected to iterative convergence; calculating deviation degree indexes of interval sample points of the sample characteristic quantity, and when the deviation degree indexes are smaller than a set threshold, defining the deviation degree indexes as transitional working conditions and removing the sample characteristic quantity and corresponding original data; outputting an original sample set according to a working condition division result which does not contain the transitional working conditions; and calculating typical parameters of harmonics of the working conditions. According to the method, the harmonic pollution level of an electric network after user access and running can be assessed more reasonably, thereby facilitating analysis and control on the harmonic users.

The invention discloses a simplified weighted-undirected graph-based statistical averaging model construction method. With the method adopted, problems such as the problem of deviation caused by the selection of a specific template image and the problems of low convergence speed and low efficiency of a template-free GroupWise registration can be solved. The method of the invention includes the following steps that: with a rigidly-aligned image set adopted as input, a vertex set of a graph model is constructed, a threshold value is solved, edges between images are constructed, so that an undirected graph model can be built; based on image affinity propagation clustering results, intra-cluster redundant edges and inter-cluster redundant edges are deleted, so that the undirected graph model can be simplified; based on the simplified graph model, the weights of edges are set according to the difference of each image and adjacent images connected with the image; deformation fields between each image and the adjacent images connected with the image are calculated through registration; the deformation fields are compounded according to the weight of the corresponding graph model; the compounded deformation fields act on the images, so that the differences between the images are gradually decreased, and the simplified weighted undirected graph model gradually shrinks; and the average image of the image set is solved, so that an atlas can be obtained.

The invention discloses a stock index tracking and prediction method based on social network clustering. The method comprises the following steps: acquiring indices of a previous month and a current month and component stock data from a third-party database, and carrying out data cleaning, so that in-sample data and out-of-sample data that can be used for researches are obtained; calculating metric distances through correlation coefficients among component stocks to construct a social network among the component stocks, carrying out the network clustering through the adaptive affinity propagation clustering algorithm, extracting a clustering center of each cluster to form a stock pool, utilizing an index tracking optimization model to achieve the optimized tracking of stocks in the stock pools on a benchmark index, and determining the optimized weight of index tracking; and applying the stock pools and the optimized weight obtained through in-sample training to the index tracking for the out-of-sample data, so that a predicted index is obtained. The invention further provides a stock index tracking and prediction system. The method and system provided by the invention has the advantages that correlation among the constructed stock pools is low, tracking errors are small, replication results are highly stable, and accurate index tracking is achieved.

The invention discloses a road congestion discovery method based on a higher-order Markov model. The method includes two steps: probability transfer matrix calculation and congestion state prediction;during probability transfer matrix calculation, obtaining appropriate road section state classification by employing affinity propagation clustering, and then calculating a transfer probability matrix of the road section state in different time intervals; and during congestion state prediction, predicting the congestion state with the combination of the transfer probability matrix according to the state of road section at which a moving object is located and adjacent road sections. According to the method, the transfer probability matrix is calculated through the affinity propagation clustering algorithm in advance and then stored in a database, when a model is required for prediction, the probability matrix is directly read from the database, the transfer probability matrix is calculatedin advance and stored, the matrix can be directly inquired during prediction, the speed of real-time prediction is greatly increased, and the classification accuracy can be improved by employing affinity propagation clustering so that the correctness of the probability transfer matrix is improved.

The invention relates to a power user grouping method and system based on the DTW distance and an affinity propagation clustering algorithm, and the method comprises the steps: collecting power quality monitoring data and user active power data, and constructing a feature matrix representing a time sequence change rule; calculating the feature similarity between each user and the power quality index, and quantifying the association degree of the user features and the power quality features; on the basis of the association degree of the user characteristics and the power quality characteristics, performing power quality characteristic similarity clustering calculation on different users, and classifying the users with similar association degrees into the same class; and providing corresponding electric energy with different electric energy qualities for users with different electric energy quality characteristics. According to the invention, user characteristic classification based on the aspect of power quality is realized, and a technical support is provided for lean power quality service of users.

The invention discloses a multi-model least square support vector machine (LSSVM) modeling method of a bearingless brushless direct current motor. According to the data driving principle, proper signals are utilized for exciting the bearingless brushless direct current motor, input and output samples are obtained, the samples are clustered according to the input set and the output set through the affinity propagation clustering, subclasses subjected to secondary clustering are subjected to the least square support vector machine fitting, a local LSSVM module is built, and further, the weighting and the form are adopted for constructing a system model of the bearingless brushless direct current motor. The multi-model LSSVM modeling method does not depend on the system mechanism and the specific parameters, a bearingless brushless direct current motor system is resolved, the modeling difficulty is lowered, and the modeling precision is improved.

The invention discloses a stippled DPM two-dimensional code area positioning method. The method comprises the steps that gray processing is carried out on an acquired image with a stippled DPM two-dimensional code to acquire a grayscale image; affinity propagation clustering ideology is used to realize automatic clustering to acquire a super pixel area; edge confidence is introduced to adjust a super pixel edge, so as to form an adaptive edge simple linear iterative clustering (AE-SLIC) algorithm; super pixels are used as the vertex of a spectral clustering central image for secondary clustering; and the super pixels in a DPM area are clustered due to high similarity, thus accurately positioning stippled DPM area is completed. After experimental testing and analysis, a super pixel segmentation result acquired in the invention is better than a common super pixel segmentation algorithm in terms of completeness, computational complexity and the like. Compared with a traditional positioning algorithm based on pixel operation, the method provided by the invention has the advantages of good instantaneity, accurate positioning and good robustness.

The invention discloses a ship weld defect detection method based on a deep convolutional neural network model. Firstly, an M-SMOTE algorithm based on the distance extreme value is adopted to carry out weld sample unbalanced data set processing, and fine generation of minority class new samples is achieved; then, a deep convolutional neural network model which comprises 13 layers and faces ship weld defect detection is constructed, the input of an input layer takes a result obtained by carrying out improved affinity propagation clustering on feature data of a ship X-ray weld image set, and input data of each middle layer of the deep convolutional neural network is subjected to adaptive PCA dimensionality reduction; then training the deep convolutional neural network model; and finally, performing weld defect detection by using the trained deep convolutional neural network model. According to the ship welding seam defect detection method, the technical difficulties of welding seam data imbalance, defect feature selection, data high dimension and the like are effectively solved, and efficient and effective detection of ship welding seam defects is achieved.

The invention discloses an affinity propagation clustering method based on a genetic algorithm, and belongs to the field of data mining. The method is characterized by comprising the following steps of: preprocessing data, and performing clustering analysis on a data set by adopting an affinity propagation clustering algorithm. The clustering analysis comprises the following steps: firstly, takinga similarity matrix formed by similarities (negative Euclidean distances) among data points as a working basis, and taking all data objects as potential clustering centers; secondly, obtaining an optimal deviation parameter and a damping factor by utilizing a genetic algorithm; and finally, continuously updating and iterating in the attraction degree matrix and the affiliation degree matrix by utilizing the obtained optimal solution until a termination condition is met, finishing clustering and obtaining a clustering result. According to the algorithm, the problem that a standard affinity propagation clustering algorithm is sensitive to deviation parameters and damping factors is effectively solved, the accuracy of the clustering algorithm is improved, the clustering number is closer to the real data set clustering number, and the algorithm can be effectively applied to clustering analysis of various types of data.

The present invention discloses an affinity propagation clustering image segmentation method based on fuzzy connectedness. The method is mainly used for solving the problems of over segmentation and low segmentation accuracy of other similar methods, which are generated due to a large target span. The affinity propagation clustered image segmentation method based on fuzzy connectedness comprises the implementation steps of: (1) performing super pixel segmentation on an image; (2) extracting spatial characteristics and density characteristics of super pixels; (3) according to the spatial characteristics of the super pixels, calculating a neighboring relationship; (4) according to the density characteristics of the super pixels and the neighboring relationship, calculating an affinity relationship; (5) according to the affinity relationship, calculating the fuzzy connectedness among all the super pixels; (6) according to the fuzzy connectedness and a spatial characteristic relationship, calculating a similarity degree among the super pixels; and (7) completing clustering on the super pixels by using affinity propagation clustering, and generating a segmentation result. The affinity propagation clustering image segmentation method based on fuzzy connectedness is a full-automatic segmentation method, has good target consistency of segmentation and high segmentation accuracy, solves the problem of difficulty in determining bias parameters of an existing AP clustering image segmentation method, and has a good segmentation ability for a nature color image.

The invention provides a communication network hotspot area capacity enhancement-oriented multi-air base station deployment method, which comprises the following steps: obtaining the state information of a target area, wherein the state information of the target area comprises the current geographic position information of a user terminal in the target area and the current geographic position information of an air base station in the target area; dividing cooperative clusters for the air base stations based on an affinity propagation AP clustering algorithm, the interference intensity between the air base stations and the state information of the target area to obtain a first user air base station association set; calculating a first system capacity based on the first user air base station association set, and setting the first system capacity as a reward function of a multi-agent deep reinforcement learning DQN network; using the air base station as an independent agent, determining the action of each agent according to the DQN network, and moving the position of the air base station according to the action; through at least one time of position movement, obtaining the air base station deployment position of the target area, and improving the capacity performance of the system.

The invention discloses a method and device for image annotation and a storage medium. The method comprises the following steps: acquiring a to-be-labeled image; performing super-pixel segmentation on the image to generate a plurality of super-pixel blocks; respectively extracting features of the plurality of superpixel blocks; inputting the features of the plurality of superpixel blocks into an affinity propagation clustering algorithm to obtain a pixel block region; and acquiring annotation information of the pixel block region. According to the method, the pixel block area is determined based on the super-pixel segmentation technology and the affinity propagation clustering algorithm, the number of categories does not need to be specified in advance, and the clustering performance and efficiency are high, so that the period of image labeling is shortened, the time and labor cost is saved, and the efficiency of image labeling is improved.

The invention discloses a mobile visual retrieval system based on key feature descriptor selection, and belongs to the field of mobile visual retrieval. The mobile visual retrieval system based on key feature descriptor selection is characterized by utilizing an affinity propagation clustering method to perform key feature descriptor selection on the feature descriptors of images to be queried, using a method of transmitting and then weighing and matching the key feature descriptors, reducing data transfer volume through key feature descriptor selection to reduce network transmission time delay and further achieving the aim of improving user experience. Meanwhile, the method can effectively eliminate noise point data of the images to be queried, thereby reducing the data transfer volume and effectively improving the retrieval accuracy at the same time.

The invention discloses a path edge identification method and system for a metal structure climbing robot of a crane, and the method comprises the steps: firstly carrying out the preprocessing of a path image collected by a robot CMOS camera, and obtaining a path grey-scale map; adopting a line segment segmentation detection algorithm to detect the path grey-scale map, all straight line segments meeting constraint rules in the path image are detected, and a straight line segment detection image is obtained; then, performing feature extraction on all straight line segments contained in the straight line segment detection image, performing clustering operation by adopting an affinity propagation clustering algorithm based on the extracted features, and screening out straight line segments forming a path edge; and finally, for all the screened straight line segments forming the path edge, extracting endpoint coordinates as fitting points, fitting by adopting a least square method, and taking the straight line segment obtained by fitting as a finally identified path edge line. The technical effect of improving the accuracy of straight line segment clustering and the accuracy of edge path recognition is achieved.

There are disclosed herein a technique for use in security. In at least one embodiment, the technique comprises receiving information relating to users and performing an affinity propagation clustering operation in connection with the information to identify a cluster of similar users. Further, the technique determines a risk in connection with a user in the cluster by comparing the user to one or more other users in the cluster. Still further, based on the risk in connection with the user, the technique controls access by the user to a computerized resource.

The invention relates to a 35kV power line fault traveling wave extraction method based on affinity propagation clustering, and belongs to the technical field of 35kV power line fault distance measurement. The method comprises the steps of data preprocessing, fault traveling wave signal characteristic parameter extraction, affinity propagation clustering calculation and the like. According to themethod, the influence of interference signals generated by power noise on a 35kV power line fault accurate positioning system is reduced, switching value signals do not need to be extracted, clustering calculation is conducted on traveling wave signals through affinity propagation clustering, and misjudgment generated by interference of the interference traveling wave signals is eliminated according to the clustering characteristics of the interference traveling wave signals. Meanwhile, the method enables the 35kV power line fault accurate positioning system to identify lightning stroke multi-source discharge points, and is easy to popularize and apply.