147results about How to "Improve clustering effect" patented technology

The invention discloses a double-criterion abnormal electricity consumption detection method based on dimension reduction clustering, and relates to the technical field of electricity consumption detection, and the method comprises the following steps: building a daily and monthly electricity consumption data set of electricity consumption users based on original intelligent electric meter data, constructing feature indexes, and carrying out the normalization to obtain a high-dimensional daily and monthly electricity consumption feature set; performing dimensionality reduction processing on the high-dimensional daily and monthly electricity consumption feature set by using a uniform manifold approximation and projection algorithm to obtain a low-dimensional projection feature set, and setting a double-criterion detection rule based on an unsupervised clustering algorithm to construct an abnormal electricity consumption detection model; and based on the abnormal power consumption detection model, inputting the low-dimensional projection feature set, outputting an abnormal power consumption detection result, and evaluating output by using a model evaluation index. According to the method, data can be better clustered, the parameter selection difficulty is reduced, and accurate optimal clustering center selection is realized, so that the accuracy of an abnormal power utilization detection model is improved.

The invention provides a field drip irrigation automatic zoning method and system based on an improved k-means algorithm. The method comprises the following steps: acquiring state data of each land parcel in a field area; inputting the state data of each land parcel into a pre-trained random forest model, and obtaining N initial clustering centers output by the random forest model, N being a clustering number of a k-means algorithm; n optimal clustering centers are obtained according to a preset angle exid optimization algorithm, the state data of each land parcel and the N initial clustering centers; according to the state data of each land parcel and the N optimal clustering centers, each land parcel is clustered, a clustering result of each land parcel is obtained, and the clustering result of each land parcel represents an irrigation type to which the land parcel belongs; and determining a drip irrigation scheme of each land parcel according to the clustering result of each land parcel. By applying the method provided by the invention, an accurate plot clustering result can be obtained, so that the drip irrigation effect can be effectively improved.

The invention discloses a pneumovirus gene data multi-view clustering integration method and device and electronic equipment. The method comprises the following steps: constructing a width self-encoder with a width learning network and a global subspace structure; training a width auto-encoder, determining model parameters of the width auto-encoder, inputting multi-view pneumonia gene data into the trained width auto-encoder, and outputting a compressed feature subspace; clustering the compressed feature subspaces to obtain a plurality of basic partitions as integration members of an integration pool; the basic partition is converted into a fuzzy partition matrix, an undirected bipartite graph is constructed based on the integration pool, and connection weights between nodes of the undirected bipartite graph correspond to values in the fuzzy partition matrix; and cutting the undirected bipartite graph into several disjoint sub-graphs, and obtaining a clustering integration result of multi-view pneumonia gene data according to the sample nodes of each sub-graph. According to the method, parameter quantity and computing resources are remarkably reduced, and multi-view clustering analysis of pneumonia gene data is efficiently completed.

The invention provides a virtual power plant resource partitioning method based on a multi-layer perceptron. The method comprises the following steps: acquiring geographical location information of each resource in a virtual power plant; adjusting a density-based clustering algorithm according to the geographical location information of each resource in the virtual power plant, and performing clustering analysis on the geographical location information of each resource by using the adjusted density-based clustering algorithm to obtain a plurality of first clustering results; counting the number of geographical location information contained in each first clustering result, adjusting the first clustering results according to the number to obtain a plurality of second clustering results, and taking a range formed by each second clustering result as a partition; and inputting the geographical location information of each resource into a pre-trained multi-layer perceptron to obtain an importance degree score of each resource. According to the method provided by the invention, each energy resource can be partitioned and effectively regulated and controlled.

The invention discloses a flight path analysis method and system fusing frequency domain features and multi-dimensional evaluation indexes. The method comprises the following steps: performing abnormal flight number filtering on flight path data acquired by a flight path data acquisition device; second track data are obtained through fast Fourier transform, and high-frequency energy features and peak frequency features are extracted; through a unified standardization processing method, target track data needing to be analyzed is screened out; adaptively determining an interval in which the optimal clustering cluster number is located; traversing each clustering cluster number in the interval, determining an optimal clustering cluster number K according to the contour coefficient and the Devens-Pall index, and performing clustering analysis through a K-Means + + algorithm according to the optimal clustering cluster number K; and carrying out visual display on a clustering analysis result of the target track data. The method can provide a theoretical reference for the clustering analysis of the arrival and departure tracks of the aircrafts in the terminal area through the analysis method fusing the frequency domain features and the multi-dimensional evaluation indexes, enables the optimization result to fit the reality, and has the practical application potential.

The invention provides a method for identifying and classifying reaction products of an alkali-activated fly ash and slag gelling system. The method comprises the following steps: acquiring a BSE image and an EDS image of an alkali-activated fly ash and slag gelling system sample; clustering based on the pixel color features of the EDS image to obtain each phase mask; fusing the phase feature of the BSE image into the EDS image, and extracting a hole structure based on the gray feature of the BSE image in the same view field area as the EDS image; according to the phase masks and the pore structures, obtaining an unreacted precursor phase mask from which the pore masks are deducted and a reaction product phase mask from which the pore masks are deducted; performing superpixel segmentation on the reaction product phase mask to obtain an Mg / Al ratio of each superpixel; and carrying out clustering analysis on the Mg / Al ratio of each superpixel to realize identification and classification of slag reaction products, mixed reaction products and fly ash reaction products. According to the method, refined classification of the reaction products in the fly ash slag alkali-activated gel system can be achieved, and the accuracy of recognition and classification of the reaction products is improved.

The human cost data storage method for the power grid engineering system comprises the steps that human cost data related to cost data in the power grid engineering system are acquired, and the human cost data comprise work type data, man-hour data and salary data of constructors; determining a DBSCAN clustering radius range based on the average man-hour salary of each type of work; based on a DBSCAN clustering radius range, performing clustering analysis in a man-hour-salary two-dimensional plane formed by the human cost data to obtain a preliminary classification result; when it is determined that the isolated data points exist based on the preliminary classification result, secondary classification is carried out on the isolated data points based on the distance between the isolated data points and the known clustering cluster, and a final classification result is obtained; and realizing partition storage of the cost data based on a final classification result. By implementing the method, optimized storage of the cost data of the power grid engineering system can be realized.

The invention belongs to the technical field of data analysis and application, and particularly relates to a multi-view clustering-oriented plug-in cross-view pre-learning method, which comprises the following steps of: firstly, obtaining coded representation of original input data on each view by adopting a multi-view automatic encoder; then, complementary consistency information among multiple views is introduced in a cross-view comparative learning mode; and finally, performing model supervision by taking multi-view reconstruction loss and cross-view contrast learning loss as total task loss through joint optimization. According to the method, by introducing cross-view comparative learning and an insertion type pre-learning method, multi-view information is fully considered, and the multi-view information is comprehensively and effectively integrated in a feature learning stage, so that the expression ability and clustering performance of the model are improved, the information loss phenomenon is reduced, and a more accurate and stable multi-view clustering result is realized.

The invention relates to a simulation service platform data feature extraction method and device and electronic equipment, and relates to the technical field of simulation data processing. Comprising the steps that a nested lattice system is constructed, lattice points of the nested lattice system have corresponding data, a lattice Gaussian distribution function is constructed based on the nested lattice system, and single-point Gaussian distribution is calculated based on the lattice Gaussian distribution function for any lattice point. Adding the single-point Gaussian distribution of each lattice point to obtain the overall Gaussian distribution of the nested lattice system, calculating a discrete Gaussian distribution group according to the single-point Gaussian distribution and the overall Gaussian distribution of each lattice point, and extracting key features of data in the nested lattice system based on the discrete Gaussian distribution group, the key features are used for clustering data in a nested lattice system, the flexibility, accuracy and efficiency of high-dimensional data feature extraction can be improved, and the accuracy and efficiency of simulation data clustering by a neural network model are improved by adopting a mathematical model to consider Gaussian distribution characteristics of nested lattices.

The invention discloses a later fusion multi-kernel clustering method and system based on tensor learning and active reconstruction, and relates to the technical field of information clustering, and the method comprises the steps: firstly obtaining a target data sample and a clustering task, mapping the target data sample to a Hilbert space, and generating a multi-kernel matrix; carrying out active reconstruction on a base division matrix in later fusion multi-kernel clustering; applying low-rank tensor constraint to the actively reconstructed basis division matrix; establishing an objective function of a later fusion multi-kernel clustering method; performing initialization operation on all variables; by utilizing an alternating direction multiplier method, updating and optimizing a certain variable in sequence by fixing other variables; the convergence threshold value of the target function is limited, and when the change fluctuation of the target function value is smaller than the threshold value, the optimization process is stopped; and taking the directly learned consistency division matrix as input, and obtaining sample label information and a clustering result through a K-means clustering algorithm. The clustering scheme provided by the invention has remarkable effectiveness and high efficiency.

The invention discloses a time series clustering method and system based on feature mining and comparative learning, and the method comprises the steps: collecting the related data of a power market transaction subject, constructing variables from a first-level index of power transaction market clustering and a subordinate second-level index, and carrying out the granularity alignment of time series data. And carrying out noise reduction and abnormal value removal processing on the time sequence data of the power consumer transaction behaviors by adopting wavelet transform. Performing data enhancement on the time sequence of the power consumer transaction behaviors, constructing a sample pair, and performing sample feature extraction by adopting an auto-encoder; and clustering the sample pair matrix after data enhancement by adopting a time sequence clustering method based on a comparative learning idea. The invention provides a unique and novel feature mining method suitable for electricity market data, and good feature extraction also improves the subsequent clustering effect. The problem that an existing time sequence data enhancement method is difficult to describe the transformation invariance of the time sequence is solved.

The invention relates to the technical field of multi-view clustering analysis, and discloses an incomplete multi-view clustering method and system, a storage medium and equipment. In order to solve the problems that a small amount of easy-to-obtain supervision information is not utilized in an existing method, and the clustering performance is deteriorated due to low view interpolation quality under high missing degree, the invention provides a technical scheme of combining paired constraint weighted interpolation and double-level feature fusion. The method comprises the following steps: firstly, screening similar samples by using pairwise constraint information, and reconstructing a missing view through similarity weighting; then extracting feature mean values and standard deviations of all views based on an encoder, aligning features through view hierarchy self-adaptive comparison learning and reserving private information, and optimizing feature distribution in combination with sample hierarchy semi-supervised loss; and finally, completing clustering through a Gaussian mixture model. According to the method, the pairwise constraint information is effectively utilized, the view recovery quality and the feature complementarity under the high-missing scene are improved, the clustering performance is remarkably improved, and the method is suitable for scenes such as data analysis of multi-view data missing and the like.

The invention provides a text clustering system for open source security intelligence. The text clustering system comprises a data acquisition module, a preprocessing module, a feature extraction module, a similarity calculation module, a clustering initialization module, an iterative clustering module, an evaluation module and a result output module. The system collects a target text, removes noise, calculates a word frequency and an inverse document frequency to construct a feature vector, calculates similarity after normalization processing, randomly selects an initial clustering center, iteratively updates the clustering center until the clustering center is stable, evaluates a clustering effect, outputs a clustering result and generates a topic tag. According to the invention, the analysis efficiency and accuracy of open source security intelligence can be improved, and security threats can be quickly identified and responded.

The invention relates to a transformer area voltage multi-dimensional feature portrait construction method based on fusion ISSA clustering. The method comprises the following steps: S1, acquiring sampling point voltage and load data of each user in a transformer area; step S2, based on the voltage and load data of the sampling point, calculating a voltage feature tag considering security, stability and economic dimensions; and S3, determining an optimal clustering cluster center combination by using a K-means clustering algorithm based on ISSA and taking the voltage feature tag as a clustering object, and distributing the voltage feature tag into a cluster to obtain a clustering result, thereby realizing construction of the transformer area voltage multi-dimensional feature portrait. Compared with the prior art, the method achieves the optimal determination of the clustering center, fully considers the multi-dimensional factors of the transformer area and the difference between the users in the transformer area, and effectively avoids the falling into a local optimal solution.

The invention belongs to the technical field of image classification, and discloses a clustering method and device for graph regular non-negative matrix factorization and a medium, and the method comprises the steps: obtaining matrix information data and a to-be-clustered number; defining four initialized sparse matrixes, respectively and correspondingly constructing a matrix E, a weight matrix U, a coefficient matrix V and an assistance matrix Z, and setting an initial hyper-parameter value mu; determining a target equation and the maximum number of iterations, and iteratively solving a construction matrix E, a weight matrix U, a coefficient matrix V, an assistance matrix Z, a Lagrange operator Y1 and a Lagrange operator Y2 according to an updating formula; and outputting a final coefficient matrix V under the condition that the number of iterations is maximum or iteration converges, and obtaining a clustering result. According to the method, the target function construction method is improved, and better constraint conditions are added to capture deep information features, so that the robustness and accuracy of image classification are improved, and a better clustering effect is obtained.

The invention relates to a federal deep clustering method and system based on differential privacy, processing equipment and a storage medium, and is characterized in that the method comprises the steps that a central server trains a global model based on a public data set and a local clustering model of each client, and a trained global model is obtained; and each client adopts the trained global model to predict the local data set to obtain a corresponding clustering distribution result, so that the method can effectively relieve adverse effects caused by isomerism, improves the clustering performance of the model, and can be widely applied to the fields of data analysis and network security.

The invention discloses an elevator fault classification method and system, and relates to the field of fault data classification. Elevator faults are classified from the single aspect of electrical faults, mechanical faults and operation faults; the method comprises the following steps: collecting state parameter data of multiple groups of historical mechanical components and electrical components of a tested elevator, clustering the state parameter data, and endowing each type obtained by clustering with a grade; a basis is provided for subsequent determination of the fault levels corresponding to the state parameter data of the mechanical assembly and the electrical assembly of the to-be-classified elevator, and then the fault category of the to-be-classified elevator is judged; the state parameter data and the operation parameter data of the mechanical components and the electrical components of the to-be-classified elevator in the future are predicted, then whether the to-be-classified elevator breaks down or not at the future moment is obtained, and then measures can be taken in time for maintenance.

The invention discloses a multi-view learning method and device based on low-rank constraint and hypergraph learning and a medium, and relates to the technical field of multi-view clustering. The method comprises the following steps: performing depth non-negative matrix factorization on a data matrix of multiple views to obtain a coefficient matrix; constructing a reconstruction error of depth non-negative matrix factorization through a norm; performing low-rank constraint on the coefficient matrix to obtain a low-rank constraint term; performing hypergraph structure learning on the coefficient matrix to obtain a hypergraph regular term; establishing a multi-view learning objective function based on the reconstruction error, the low-rank constraint term and the hypergraph regular term; and solving the objective function to obtain a learning result of the multiple views. Low-rank constraint is used for a coefficient matrix, sparsity of the coefficient matrix is enhanced, redundant information of the coefficient matrix is removed, a hypergraph structure is learned while the coefficient matrix is constructed, multivariate relations between data points are learned, manifold structure information of a source space is reserved, expression of the coefficient matrix on data deep information is enhanced, and then accuracy of a clustering result is improved.

The embodiment of the invention provides a clustering method and device of multiple view data, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring view data corresponding to a plurality of views, projecting the corresponding view data to a low-dimensional space based on a projection matrix of the view for each view to obtain a subspace representation matrix and an error matrix, and calculating to obtain local total information according to the subspace representation matrix and a weight matrix, and constructing an optimization target item according to the nuclear norm of the subspace representation matrix, the preset norm of the error matrix and the local total information, solving an optimization target function to obtain a representation optimization matrix corresponding to each view, and performing data clustering according to all the representation optimization matrixes to obtain at least one clustering result. In the dimension reduction process, subspace representation is synchronously carried out, in the process of optimizing the target function, mutual promotion and co-evolution of the projection matrix and the subspace representation matrix are achieved, the accuracy of subspace representation is improved, and the clustering quality is improved.

The invention discloses a disease control big data analysis method and system, and the method comprises the following steps: obtaining a data set X comprising a plurality of class attributes, and carrying out the preprocessing; performing independent optimal sequence search on each category attribute in the data set to obtain an optimal sequence, determining a distance structure suitable for disease control big data analysis, calculating distance measurement between a sample and a cluster according to the obtained optimal sequence, and constructing a clustering model; and by minimizing a target function, updating a sample attribution matrix and sequence, and dynamically adjusting a clustering result. Compared with the prior art, the method has the advantages that the system can adaptively adjust the clustering result by dynamically learning the optimal value sequence of the data and the corresponding distance structure, so that the clustering accuracy is remarkably improved, and visual interpretation is provided for understanding of the data.

The invention relates to the technical field of signal processing, data characterization and classification, and discloses a radar signal spectral clustering sorting method based on SOM anchor point extraction and graph fusion, which comprises the following steps of: configuring a radar signal sorting cluster number and radar pulse parameters, obtaining a normalized radar pulse data set, and storing the normalized radar pulse data set; taking each normalized radar pulse in the radar pulse data set as a node, and constructing a KNN graph of the radar pulses; extracting anchor points of the normalized radar pulse data set based on SOM, calculating the similarity between the extracted SOM anchor points and all nodes to obtain a similarity matrix, and constructing an anchor map adjacent matrix based on the similarity matrix, namely a self-adaptive anchor map; performing weighted fusion on the KNN graph and the self-adaptive anchor graph to obtain a fusion graph; and performing spectral clustering sorting based on the radar signal sorting cluster number and the fusion image to obtain a sorting result. According to the method, the limitation that only distance information and density information can be utilized in a classic radar sorting clustering algorithm is broken through, and the sorting performance under the condition of complex radar pulse distribution is improved.

The invention provides a water supply pipe network pressure measuring point optimization method based on structured deep clustering, and relates to the technical field of pipe network pressure monitoring, the method obtains high-dimensional feature representation of original data through an SDCNs model, removes redundant information in the original data, then fuses high-dimensional features and structural features in the data, and finally optimizes the pressure measuring points of the water supply pipe network. The SDCNs learn the structural features of the data while learning the high-dimensional features of the data, so that the SDCNs can obtain a good clustering effect on the pressure monitoring point arrangement problem, initial populations of a traditional GA algorithm and a PSO algorithm are optimized based on the good clustering effect, a better pressure monitoring point arrangement scheme is obtained, and the pressure monitoring point arrangement efficiency is improved. The SDCNs can be effectively applied to the arrangement scheme of the pressure monitoring points of the water supply pipe network, and the space positions and the number of the monitoring points are optimized, so that the leakage monitoring capability of the pipe network is improved, and the balance of economy and effectiveness of a monitoring system is realized.

The present invention relates to the field of big data analysis technology, and in particular to a method for optimizing a scenario based on a teacher-student ARG game social platform, the method comprising: obtaining learning dimension data of each target student in each preset time period under each preset dimension, randomly combining all preset dimensions, and clustering the learning dimension data under each data dimension combination; determining a learning state characterization index for each target student for each data dimension combination; determining an overall state characterization index for all target students for each data dimension combination; screening out a target dimension combination according to the overall state characterization index and a target clustering cluster; optimizing the teacher-student ARG game social platform scenario according to the target dimension combination. The present invention realizes adaptive dimensionality reduction of multiple preset dimensions by analyzing a large amount of learning dimension data, thereby improving the rationality of optimizing the teacher-student ARG game social platform.

The present invention provides a clustering method, device, electronic device and storage medium for work order data, the method comprising: obtaining work order data; inputting the work order data into a hierarchical clustering model to obtain a clustering result of the work order data; wherein the hierarchical clustering model comprises a classification layer, a first clustering layer and a second clustering layer; the second clustering layer comprises a SimCSE sublayer and a DEC clustering sublayer, wherein the SimCSE sublayer is obtained by supervised training based on a work order sample and a text vector of the work order sample; the DEC clustering sublayer is obtained by training based on the text vector of the work order sample and the clustering result of the work order data corresponding to the text vector. The present invention improves the clustering speed and clustering effect of large amounts of work order data.

The invention relates to a clustering method which is used for electronic equipment, and the method comprises the steps that a first cluster and a cluster set are initialized, the cluster set comprises the first cluster, and the first cluster comprises a first node in a plurality of nodes to be clustered; for each of the other nodes in the plurality of nodes: determining the number of clusters communicated with the node in the cluster set; based on the number, deleting the nodes or combining the nodes with clusters in the cluster set; and taking the cluster set as a clustering result. The invention also relates to a content recommendation method, a clustering device, a content recommendation device, a medium and a computer program product.

The present application discloses a multi-view clustering method, system, device and medium based on non-negative matrix decomposition. The method obtains the original data matrix of multiple views; performs multiple non-negative matrix decompositions on the original data matrix to obtain a clustering target matrix and a spatial basis matrix; constructs a first target loss function and a second target loss function based on the original data matrix, the clustering target matrix and the spatial basis matrix, wherein the first target loss function is used to indicate denoising of the original data matrix, and the second target loss function is used to indicate feature importance evaluation of the original data matrix to select features according to feature importance; and uses an alternating direction multiplier method to iterate the first target loss function and the second target loss function to obtain a target clustering result. The present application can improve the accuracy of clustering results, thereby improving the clustering effect.

The embodiment of the invention discloses an airport flight plan strategy delay prediction method based on delay disassembly and assembly, relates to the technical field of air control and planning, and can timely and effectively predict the delay of arrival and departure of an airport flight plan. The airport flight plan strategy delay prediction method based on delay disassembly and assembly is used for obtaining an airport capacity probability graph by using an airport flight plan and historical flight operation data, and obtaining an airport flight arrival and departure delay prediction value according to the airport flight plan, airport operation mode modeling and the airport capacity probability graph. The method is suitable for evaluating the rationality and effectiveness of airport flight time / planning at the strategic stage.

The present invention proposes an online monitoring and diagnosis system for hydropower equipment, which relates to the field of monitoring and diagnosis of hydropower equipment, and includes: a data acquisition module for collecting real-time data; a data processing module for performing error detection using a verification method to obtain verified data; a data analysis module for performing clustering analysis using a clustering algorithm, marking normal data and abnormal data according to the clustering results, and obtaining clustered data; a model analysis module for performing feature extraction on the clustered data according to a pre-trained autoencoder, and performing secondary clustering on the clustered data according to the features extracted by the autoencoder to obtain the results of the secondary clustering; an abnormal diagnosis module for matching the results of the secondary clustering based on a pattern matching algorithm, determining the diagnosis type, and outputting a diagnosis report. The present invention can overcome the limitations of traditional methods, provide more accurate and timely fault identification, and can handle complex and new fault modes.

This application provides an image processing method, apparatus, computer-readable medium, and computer device. The image processing method includes: acquiring multiple original images and descriptive text corresponding to each of the multiple original images; converting the descriptive text corresponding to each of the multiple original images into text vectors to obtain text vectors corresponding to each of the multiple original images; performing clustering processing based on the text vectors corresponding to each of the multiple original images to obtain multiple original image clusters, and generating text embedding vectors corresponding to each original image cluster; using the text embedding vectors corresponding to each original image cluster as text-guided features, performing semantic editing processing on the original images in each original image cluster to obtain processed images, wherein the processed images are used as sample images for a machine learning model. The technical solution of this application embodiment can enrich the diversity of training datasets, thereby improving the generalization ability and robustness of the model.

This disclosure provides a matching model training method, device, and medium, relating to the field of computer technology. The method includes: clustering the original data in an unlabeled target dataset using a clustering algorithm to obtain multiple data clusters. The target dataset is obtained by combining two unlabeled candidate datasets. Each pair of original data from the multiple data clusters is concatenated to obtain N concatenated data. Then, each pair of concatenated data from the N concatenated data is combined to obtain K data groups. The label value of each data group is determined based on the clustering of the original data within each data group. An untrained matching model is trained based on the K data groups and the label values ​​of each data group until a trained matching model is obtained. Labels are added to the unlabeled data through clustering and data sources, and the effect is optimized through an iterative training process, ultimately resulting in a more accurate trained matching model.