28 results about "Semi supervised clustering" patented technology

The invention discloses an attention deficit hyperactivity disorder subtype recognition method based on brain network topology hub deviation, relates to the technical field of medical diagnosis, and has the technical key points that a norm model of a brain structure form similarity network is constructed based on multi-center big data; quantifying a brain network topology hub index as a target phenotype of the norm model; then, performing semi-supervised clustering analysis by utilizing the individual deviation phenotype of the topological hub index so as to divide different biological subtypes and reveal unique clinical and biological characteristics of the biological subtypes; finally, strict cross validation is carried out in an external independent queue, and it is ensured that the potential biological subtypes recognized by the typing model have good generalization and clinical effectiveness.

The invention belongs to the technical field of civil aviation data analysis, and particularly relates to an aviation trajectory semi-supervised clustering method based on a Transform noise reduction auto-encoder. Comprising the following steps: S100, preprocessing a flight arrival and departure trajectory data set; s200, data set feature extraction; s300, constructing a model and training the model; s400, carrying out a semi-supervised clustering algorithm; and S500, training the model constructed in the step S300 to obtain a new test set, extracting the data features of the entry and departure and runway numbers according to the step S200, inputting the track data into the model, and clustering the output noise reduction track by a semi-supervised clustering algorithm to obtain a clustering result. According to the method, data mining is carried out on the flight path data of the airspace of the terminal area, a flight path clustering model is constructed, the mode category to which the flight path belongs is identified, and then reference is provided for fine safe operation of the airport terminal area.

The invention discloses a radiation source new individual identification method based on signal enhancement double contrast learning, and belongs to the field of software radio. According to the method, a multi-physical domain enhancement operator sequence is adopted, so that the risk that feature distortion is possibly introduced in traditional data enhancement is overcome, and a high-quality feature discrimination basis is provided for subsequent comparative learning by constructing positive and negative sample pairs with strong correlation. A feature decoupling architecture of double comparative learning is adopted, and the category discrimination advantage of supervised comparative learning and the feature decoupling capability of unsupervised comparative learning are organically fused. Dynamic semi-supervised clustering is realized by adopting a dual decision clustering mechanism, firstly, the number of potential categories is determined by utilizing density peak clustering DPC, secondly, a semi-supervised decision module is introduced to realize known category accurate identification and unknown category adaptive discovery of radiation source individuals, and then new radiation source individual identification is realized. The method is suitable for the field of software radio, and high-precision radiation source new individual identification is realized.

Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.

The invention discloses a short text unbalanced classification method based on semi-supervised clustering and dynamic resampling, which comprises the following steps of: S1, performing cleaning and feature extraction on original short text data, removing features of which the missing rate exceeds 20%, performing vectorization expression on residual texts, and constructing an initial feature space; s2, under a'compact cluster 'assumption, based on the guidance of marked data, performing iterative segmentation on majority class and minority class samples by adopting a semi-supervised hierarchical clustering algorithm, generating a plurality of discontinuous clusters, and revealing inter-class and intra-class unbalanced distribution characteristics; s3, based on a clustering result, performing dynamic under-sampling on majority of samples; s4, based on a clustering result, carrying out dynamic oversampling on minority class unmarked data; according to the method for introducing semi-supervised clustering into short text unbalanced classification for mixed sampling, the marked data and the unmarked data are clustered, the basic distribution characteristics of the short text data are effectively captured, and follow-up sampling is facilitated.

The application provides a charging facility fault early warning model construction method based on multi-source data perception, and belongs to the technical field of charging facility fault early warning.The application forms a multi-source time series data set by collecting charging pile operation state data and environmental data and performing time series alignment, expands fault samples by using an adversarial generative network and a semi-supervised clustering algorithm to form a balanced sample data set, constructs a fault early warning model containing a time series memory pool encoding layer and a frequency domain convolution feature extraction layer, cooperatively optimizes early warning accuracy and response time delay by using a double-layer game optimization framework to obtain an optimal parameter combination, deploys the optimized model to an edge computing module to realize real-time fault early warning, establishes an online incremental learning mechanism based on sliding time window detection data distribution offset, and uses an elastic weight consolidation technology to update model parameters and retain historical knowledge, thereby solving the problem of early warning performance degradation of charging facility fault early warning in a data distribution evolution environment.

This invention relates to the field of data representation technology, and discloses a semi-supervised clustering method and its open-ended question answer text encoding method. The semi-supervised clustering method includes: acquiring a dataset to be clustered, its labeled dataset, and its unlabeled dataset; mapping the dataset to be clustered to a spatial density map and / or a topological density map; and using the labeled and unlabeled datasets to cluster the data in the dataset to be clustered into several clusters, wherein each data point in each cluster has a cluster label, which can be an existing label or a new label. This invention uses a semi-supervised clustering method to efficiently and accurately cluster open-ended question answer text data, and can discover new classes with limited prior knowledge. After clustering, keywords can be extracted and encoded from the open-ended question answer text of each class, facilitating a quick and accurate understanding of the situation of the interviewed group and improving the efficiency and quality of diagnosis and treatment.

The invention discloses an oral lichen planus molecular subtype based on a serum protein marker. The molecular typing is based on a multi-center clinical queue, a new molecular typing is obtained by using transcriptome sequencing data through a Seed-Kmeans semi-supervised clustering algorithm, and a serum marker is further screened out through multiple omics data including the transcriptome sequencing data, proteome sequencing data and serum enzyme-linked immunosorbent assay experimental data. The method can be used for further judging molecular typing. The molecular typing can obviously improve the curative effect of the oral lichen planus medicine, and is beneficial to the realization of OLP individualized precision medical treatment.

The invention relates to a reservoir prediction method and device based on mixed probability principal component analysis fusion attributes, and relates to the technical field of oil and gas resource exploration and development, and the method comprises the steps: obtaining an original three-dimensional seismic data volume, well point logging data and known reservoir feature data of a target research area; preprocessing the original three-dimensional seismic data volume and then constructing an attribute matrix, and modeling the attribute matrix by using mixed probability principal component analysis to obtain a fusion attribute matrix; taking the fusion attribute matrix as an input feature, taking known reservoir feature data as a target variable, and establishing a mapping model by adopting semi-supervised K-means clustering in combination with well point label guidance classification; and calculating all data in the fusion attribute matrix of the research area according to the mapping model to obtain a global reservoir feature prediction result. The reservoir prediction method provided by the invention has the advantages of high reservoir prediction precision, high reliability and uncertainty quantification capability.

Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.

The embodiment of the invention discloses an abnormality detection method and device for an aluminum electrolysis cell, belongs to the technical field of artificial intelligence detection, and is used for solving the problem that the accuracy of recognizing the abnormality of the aluminum electrolysis cell is relatively low. Comprising the steps that multi-dimensional operation data of the aluminum electrolysis cell are obtained, the operation data are preprocessed, a multivariable time sequence is obtained, and the operation data comprise data in the operation process of the aluminum electrolysis cell; detecting the time sequence by using an unsupervised learning method, and identifying abnormal candidate data; semi-supervised clustering processing is carried out on the abnormal candidate data according to different working condition categories, similar clusters of the abnormal candidate data under the different working condition categories are obtained, and the similar clusters comprise normal prediction data and abnormal prediction data; the abnormal prediction data in the similar clusters are input into a pre-trained classification model, abnormal data corresponding to the aluminum electrolysis cell are output, the classification model comprises a full supervision model, and the abnormal data comprise data points or subsequences deviating from a normal mode.

The invention discloses an adaptive graph learning fuzzy clustering method based on low-rank matrix decomposition in the medical field, and the method comprises the following steps: carrying out the preprocessing of collected original medical image data, and forming an image feature matrix X; establishing an LAGFC objective function f which is transformed by utilizing a Feychel conjugate technology; iteratively optimizing and updating parameters in the loss function of the LAGFC objective function f in sequence until convergence or reaching the maximum iteration number; and reading the position of the maximum value of the clustering indication matrix F to obtain a clustering label. Through an adaptive graph learning mechanism and a matrix decomposition-fuzzy clustering unified framework, and through low-rank matrix decomposition guided graph learning and fuzzy clustering joint optimization, the data representation discrimination and clustering robustness are synchronously improved, so that more stable, efficient and interpretable semi-supervised clustering is realized, the clustering precision and generalization ability are improved, and the clustering efficiency is improved. The method is especially suitable for medical image data analysis tasks of high-dimensional and complex structures.

The invention discloses a forest fire risk prediction non-fire-point sample point selection method and system based on a semi-supervised clustering membership degree. The method comprises the following steps: step 1, obtaining forest fire point data; step 2, fire point environment feature extraction; 3, candidate non-fire point sample points are generated, and environment features of the candidate non-fire point sample points are extracted; step 4, membership degree calculation based on a semi-supervised clustering idea; step 5, non-fire point sample point selection based on the membership degree; and step 6, using the non-fire point prediction sample for forest fire risk prediction model training. According to the forest fire risk prediction method, by means of the semi-supervised clustering thought, the membership degree of the environment feature of each position to the forest fire point environment feature is calculated, the representativeness of non-fire point samples is improved, the influence of other variables (such as meteorological changes and human activities) on forest fire risks is researched and predicted, the sample data quality of forest fire risk prediction is effectively improved, and the forest fire risk prediction efficiency is improved. And the model precision of forest fire risk prediction is improved.

The application provides a vehicle overlapping group detection method based on a graph attention automatic encoder, and belongs to the technical fields of Internet of Vehicles and social networks. The technical scheme comprises the following steps: step 1, inputting network topology structure and node attribute information into an encoder part of a graph attention automatic encoder; step 2, performing encoding on the input network topology structure and node attribute information by a graph attention mechanism of the encoder; step 3, combining new node embedding representation with original embedding representation by a decoder to restore the network topology structure and the node attribute information; step 4, further adjusting node embedding by using a modularity optimization enhancement module to optimize constraints; and step 5, performing semi-supervised clustering by using prior information to obtain a social group detection result based on node embedding representation. The application has the beneficial effect of effectively improving the accuracy of overlapping social group detection of vehicle nodes.

The invention discloses a power transmission conductor galloping monitoring method and system, computer equipment and a storage medium. The method comprises the following steps: acquiring attitude monitoring data and surrounding meteorological data of a power transmission conductor in a jurisdiction range; acquiring operation state data related to the transmission line service, associating the attitude monitoring data, the peripheral meteorological data and the operation state data of the same transmission line acquired at the same monitoring node, and generating a data sample; performing iterative training on the semi-supervised clustering model by using the collected data samples, and outputting a clustering result; and determining whether the target transmission conductor corresponding to the newly collected data sample gallops or not according to the clustering result. According to the application, online learning training is carried out through the semi-supervised clustering model, monitoring of transmission conductor galloping is realized, especially monitoring of micro galloping can be realized, early discovery, early disposal and early regulation of transmission conductor galloping are realized, and the capability of a transmission line to resist natural disasters is improved.

The present invention relates to the field of electric power customer service technology, and is a method and system for predicting user willingness to participate in demand-side response based on semi-supervised clustering. The method comprises: obtaining a data set of electricity consumption characteristics of demand-side response users and performing data preprocessing; dividing labeled data into a must-connect constraint set and a do-not-connect constraint set; inputting the data into a training semi-supervised clustering model based on pairwise constraints and KL divergence, iteratively calculating the center points of the two clusters and the membership matrix; judging the category to which the electric power user belongs based on the membership matrix to obtain a clustering result; statistically analyzing the distribution of the two types of labeled samples in the clustering result; inputting the user-related characteristic data for which the willingness to participate in demand-side response needs to be predicted into the trained model, and calculating the membership of the user sample to "willingness to participate in demand-side response". The present invention uses information entropy and KL divergence to characterize the pairwise constraint relationship, fully utilizing the existing small amount of labeled data information, and improving the accuracy of the clustering algorithm.

The application discloses a kind of based on the incremental semi-supervised image clustering method and system of double-layer label propagation, mainly used to solve the problem of low efficiency caused by repeated calculation when static semi-supervised clustering method faces incremental image data and incremental pairwise constraint.This application uses double-layer label propagation to process the clustering problem of increasing image data and constraint condition.In the first layer label propagation, propagate and diffuse pairwise constraint information in image data samples, and combine the membership matrix of component of image data samples at the last time, incrementally calculate the membership matrix of component of image samples at the current time.In the second layer label propagation, use the clustering result at the last time to mark cluster label information in component, and let known cluster label information propagate in component structure, then gradually expand cluster label information to the whole image dataset through the membership relationship of image sample pair component, so as to realize effective semi-supervised clustering of incremental image data.

The application discloses a tobacco slitting method based on multi-view weight learning and a storage medium, obtains a hyperspectral image of target batch tobacco and performs division; an effective area of the tobacco is obtained by using a threshold segmentation method, the tobacco is divided into a set number of subareas with the same longitudinal length, characteristic spectra of the subareas corresponding to the tobacco are calculated, and a tobacco spectrum database is constructed; spectrum data in the tobacco spectrum database is subjected to band division, different band combinations are obtained, and a multi-view tobacco spectrum database is constructed; a semi-supervised clustering algorithm model is constructed based on multi-view weight and similarity learning, the semi-supervised clustering algorithm model is trained through the multi-view tobacco spectrum database, and a semi-supervised clustering algorithm model with an optimized target function is obtained; and a tobacco segmentation result is obtained through the semi-supervised clustering algorithm model. Through construction of the multi-view of the tobacco, weights are allocated to each view, differences between different tobaccos are accurately quantified, and the tobacco slitting effect is ensured.

The application discloses a kind of unknown protocol identification method and system based on semi-supervised clustering, the method includes: collecting different protocol labeled and unlabeled traffic data and extracting flow statistical feature vector and fingerprint feature vector;According to flow correlation, construct constraint information using labeled and unlabeled data, obtain must connect constraint set, do not connect constraint set, equivalence class set;Using must connect constraint set and do not connect constraint set, the Laplace score of each flow statistical feature is calculated to select features, and the flow statistical feature after feature selection is fused with fingerprint feature to obtain single flow feature vector;With equivalence class set, do not connect constraint information as guide, labeled and unlabeled single flow is mixed to carry out semi-supervised clustering;Using the traffic cluster after clustering constructs classifier.The application can more accurately represent the multi-dimensional characteristics of network traffic through feature selection and feature fusion.In the case of data scarcity, potential information in unlabeled data is mined by constructing constraint information, the identification effect of unknown protocol traffic is improved, and the dependence on labeled data is reduced.

The application belongs to the technical field of computer simulation and method optimization, and discloses a small sample semi-supervised clustering method based on separation degree, which comprises the following steps: step 1, clustering center calculation; step 2, separation degree calculation; step 3, non-target clustering center determination; step 4, parameter calculation; step 5, class center migration; step 6, iterative calculation; and step 7, target separation. The application is aimed at the problem that traditional unsupervised clustering and semi-supervised clustering are poor in target classification and discrimination performance, and a small sample semi-supervised clustering method based on target separation degree is invented, the separation degree of the target and the non-target is set, and iterative operation is carried out on the basis of the known small sample based on the separation degree, so that the accurate separation of the target and the non-target with high similarity is realized. The application adopts a new algorithm framework to improve the accuracy of target classification and discrimination.

The invention provides a semi-supervised clustering method and device, a computer program product and a clustering system. The method comprises the steps that samples and constraint pairs are obtained, the samples are records using credit cards, and the constraint pairs are rule representations that the two samples should be clustered into the same cluster or different clusters; expanding the constraint pair by adopting a union search set technology to obtain an expanded constraint pair; the influence degree of the samples is calculated to obtain an influence score, and the influence score is the influence degree of the samples on clustering; the influence scores are ranked from large to small, a preset number of samples before the ranked influence scores are determined as centroids, semi-supervised clustering is carried out according to the centroids and the expanded constraint pairs, clustering results are obtained, and the clustering results are used for identifying groups with different credit risks. According to the scheme, the problem that the accuracy of a clustering result is low due to the fact that the accuracy of mass center selection is low in the clustering process in the prior art is solved.

The invention discloses a semi-supervised clustering modeling method and system for reservoir prediction. In order to solve the problems of large seismic attribute data volume, high redundancy, label scarcity and low signal-to-noise ratio, pairwise constraint guidance and sparse feature weighting are simultaneously embedded into an improved K-means framework, clustering distribution and attribute weight are iteratively optimized, and unification of inter-class difference maximization and intra-class difference minimization is realized; differential privacy noise is introduced, so that the data security is ensured, and the precision is not obviously reduced; downsampling acceleration and multi-layer three-dimensional label alignment are supported, and million-level data can be efficiently processed. Compared with conventional K-means and waveform clustering, the method has the advantages that the blind well testing precision is remarkably improved, micro-amplitude structures such as water channels and riverbeds are clearly depicted, and a high-resolution and strong-interpretation integrated solution is provided for reservoir distribution, thickness, oil-gas possibility and sedimentary facies analysis.

This invention discloses a multi-level clustering algorithm for pedestrian trajectory flows that takes into account information from multiple cameras. It relates to a semi-supervised multi-level clustering algorithm for video target geographic flows. The method is based on a flow space and uses a hierarchical representation of video target trajectories to obtain geographic flow segment sets at different levels. Using camera IDs as labels, semi-supervised clustering is performed on these segments at different levels to obtain a set of cluster centers for each level. These cluster centers effectively describe the overall trend of a large number of geographic flows. This method overcomes the limitations of traditional algorithms, which can only perform single-level clustering and do not consider camera information. A series of experiments demonstrate the effectiveness of this algorithm.

The invention belongs to the technical field of mental and psychological assessment, and particularly relates to a method for assessing a mental state based on a multi-modal physiological electric signal, which comprises the following steps of: acquiring the significance level of each feature observation value in a second feature matrix by utilizing statistical test analysis; utilizing statistical test to retain feature observation values with significance levels meeting a first threshold value, and obtaining a target feature matrix; judging whether the feature observation value of the target feature matrix meets a preset condition or not; if yes, inputting the target feature matrix into a detection model; if not, performing feature independence processing on the second feature matrix, and inputting the processed second feature matrix into the detection model; and outputting a detection result by using the detection model. And a fitting method under a semi-supervised clustering model is adopted to realize prediction of a continuous gradually-changing mental state.

The invention relates to a face age classification method based on semi-supervised clustering disambiguation partial mark learning, and the method comprises the steps: obtaining a to-be-detected face image, inputting the to-be-detected face image into a one-to-one decomposition classifier, obtaining an augmented feature vector, inputting the augmented feature vector into a stacking-oriented classifier, and carrying out the one-to-one decomposition of the to-be-detected face image; the class mark with the maximum confidence coefficient is obtained and serves as final prediction to be output, the binary classifier is obtained through binary classification data set training, the binary classification data set is obtained through partial mark training set clustering of the face image, and the stacking-oriented classifier is obtained through prediction result training of the binary classifier. According to the method, the advantages of the partial mark learning and the semi-supervised clustering learning are comprehensively considered, the problem that the feature space and the mark space are not fully utilized in the current partial mark learning is solved, and the accuracy of face age classification is greatly improved.

The invention relates to a working condition discriminating and dosing method based on flotation froth image recognition, belongs to the technical field of flotation working condition intelligent recognition, and solves the problems that industrial field labels are scarce, and an existing clustering model is difficult to reflect actual working conditions. Comprising the following steps: collecting a flotation froth image, dividing a data set, and carrying out unsupervised clustering on an unsupervised training set to obtain an unsupervised clustering result; constructing a working condition coupling function, subdividing an unsupervised clustering result according to the working condition coupling function value of each image, and endowing a working condition false label; taking the subdivision result as a semi-supervised clustering initial center, and carrying out semi-supervised clustering on the semi-supervised training set according to a working condition pseudo label to obtain a semi-supervised clustering result to form a new clustering center; acquiring a real-time flotation froth image, and comparing the real-time flotation froth image with the new clustering center to obtain a working condition discrimination result; and adjusting dosing parameters according to the working condition judgment result. Working condition identification and intelligent dosing of the foam image are realized, and the stability and working condition interpretability of the model are improved.

The application discloses a kind of self-adapting semi-supervised deep clustering method, belong to image data clustering analysis technical field, by using labeled data training model;The trained model is again trained to the unlabeled training data, and the classification features of the data are obtained, and its classification features are converted into probability value;The features obtained by labeled data and unlabeled training data in the last hidden layer of model are semi-supervised clustering to obtain the similarity between unlabeled training data and cluster center, and the probability value and the similarity corresponding label consistent are given its pseudo label;Self-adaptively select the data with high confidence from pseudo label data and add to labeled data to participate in the next model iteration training, until model converges, then training ends;Finally, new test data is clustered and tested.Compared with other clustering methods, the application can improve the accuracy of model clustering on different data sets.

This invention discloses a semi-supervised clustering modeling method and system for reservoir prediction. Addressing the challenges of large seismic attribute data volume, high redundancy, scarce labels, and low signal-to-noise ratio, this invention embeds a modified K-means framework simultaneously with pairwise constraint guidance and sparse feature weighting. It iteratively optimizes cluster assignment and attribute weights, achieving a balance between maximizing inter-class differences and minimizing intra-class differences. Differential privacy noise is introduced to ensure data security without significantly reducing accuracy. It supports downsampling acceleration and multi-layer 3D label alignment, enabling efficient processing of millions of data points. Compared to conventional K-means and waveform clustering, this invention significantly improves the accuracy of blind well testing, clearly characterizing micro-structures such as channels and riverbeds, providing a high-resolution, highly interpretable integrated solution for reservoir distribution, thickness, hydrocarbon content, and sedimentary facies analysis.