112 results about "Canopy clustering algorithm" patented technology

The trend of analyzing big data in artificial intelligence requires more scalable machine learning algorithms, among which clustering is a fundamental and arguably the most widely applied method. To extend the applications of regular vector-based clustering algorithms, the Discrete Distribution (D2) clustering algorithm has been developed for clustering bags of weighted vectors which are well adopted in many emerging machine learning applications. The high computational complexity of D2-clustering limits its impact in solving massive learning problems. Here we present a parallel D2-clustering algorithm with substantially improved scalability. We develop a hierarchical structure for parallel computing in order to achieve a balance between the individual-node computation and the integration process of the algorithm. The parallel algorithm achieves significant speed-up with minor accuracy loss.

The invention discloses a block chain system consensus method and device based on an improved clustering algorithm. The invention relates to the field of block chains. The method comprises the following steps: enabling the first clustering center node to receive a plurality of requests, packaging the plurality of requests into blocks; broadcasting to the subconsensus cluster to which the subconsensus cluster belongs for consensus; if the block passes the consensus, enabling the block to pass the consensus, broadcasting the blocks to a backbone consensus cluster for consensus; if the consensusof the backbone consensus cluster is passed, enabling each clustering center node to digitally sign and broadcast the block; packaging the received digital signature set and the block into a submission message, broadcasting the submission message to a sub-consensus cluster to which the submission message belongs, receiving the submission message by each non-clustering center node, verifying the digital signature set, and if the transaction content of the block is verified and executed, storing the block in a chain. According to the block chain system consensus based on the improved clusteringalgorithm, the communication frequency and the data transmission amount required by single consensus are reduced, and the working efficiency of the whole block chain system is greatly improved.

The invention relates to the voltage control of the electric power field and especially relates to a reactive voltage partitioning method based on spectral clustering. A topologic matrix with a weight is used to construct a simplified power grid model. According to a spectral clustering definition, a Laplace matrix is acquired. Through an improved K-means clustering algorithm, clustering is performed on different characteristic vectors in a characteristic matrix. During clustering, modularity Q is introduced to be taken as an index of measuring an area partitioning quality. A partitioning scheme with a largest modularity Q value is selected as an initial partitioning scheme. Connectivity verification and reactive verification are performed on each area of the initial partitioning scheme. If the area can not simultaneously satisfy two conditions of area static state reactive balance and an enough reactive reserve margin, under the condition that a value of partitioning modularity Q does not change greatly, node adjusting is performed till that all the verification conditions are satisfied. In the invention, a topology structure of a complex power grid is embodied, calculating complexity is reduced, an integration evaluation system is established based on the modularity, the reactive balance and a reactive reserve index, and integration verification is performed on a partitioning result so as to ensure feasibility of the partitioning scheme.

The invention relates to a method for diagnosing photovoltaic power station faults on the basis of a fuzzy clustering algorithm, and belongs to the technical field of electrical power engineering. The method comprises the following steps: on the basis of a fault knowledge library system, matching and combining the data of a fault alarming sample to be tested with similar data samples of various fault types in the fault knowledge library system to form a fault data matrix which serves as the input quantity of an algorithm, wherein the data samples form an established fact; and according to an output fuzzy membership matrix, automatically comparing membership between the fault alarming sample to be tested and the established-fact data samples of various fault types by the algorithm, and taking the fault type with the highest membership as the fault type which is represented by the fault alarming sample to be tested so as to finish the diagnosis of the photovoltaic power station faults. The method can quickly diagnose the fault type, improve fault diagnosis accuracy and improve the reliability and the stability of the photovoltaic power station, provides fault information and an overhauling scheme for power station operation and maintenance staffs in time so as to reduce losses caused by the faults, and owns a higher utilization value.

The invention relates to a method for realizing operating state analysis and fault diagnosis of a photovoltaic array based on a density-based clustering algorithm. The method comprises the following steps: firstly collecting a plurality of electrical parameters of a maximum power point of a photovoltaic power generation array during daily work so as to obtain an electrical parameter sample combination per day; normalizing the electrical parameter samples so as to obtain a test sample combination; calculating the normalized test sample combination so as to obtain a distance matrix; automatically clustering the test samples by adopting the density-based clustering algorithm so as to obtain a plurality of clusters; respectively calculating the minimum distance between each group of reference data and each cluster based on reference data obtained by a simulation model in advance so as to form a distance vector; and finally, comparing each element in the distance vector with a cutoff distance in the clustering algorithm, and identifying a work type to which each cluster belongs. According to the method disclosed by the invention, accurate fault diagnosis can be directly realized by clustering the daily operation data of the photovoltaic system.

The invention discloses a multilayer bitmap color feature-based image retrieval method. In the method, fast clustering is performed on an image with rich color information to obtain rational statistical distribution centers of each color cluster, and based on the rational statistical distribution centers, features capable of reflecting color differences among different distribution layers of the image are extracted to perform image retrieval. The method comprises the following steps of: first performing meshing on a color space of the queried image, counting the numbers of pixel points in each mesh and selecting the mesh with a number local maximum; then quickly generating each color cluster and the rational statistical distribution centers thereof by adopting a novel distance optimization algorithm and an equal-average nearest neighbor algorithm search (ENNS) algorithm in a K-average clustering algorithm, and on the other hand, performing space sub-block division on the queried image and calculating a Gaussian-weighted color average of sub-blocks; next comparing the color average of the image sub-blocks with the rational statistical distribution centers of the color clusters to extract the features of a K-layer bitmap; and finally performing the matched searching of the image features by combining the similarity measurements of the rational statistical distribution centers of the color clusters and the bitmap.

The invention discloses a text clustering method. The method comprises the steps of receiving a text clustering instruction sent by a user; pre-training a pre-determined initial language model by utilizing the to-be-clustered corpus to obtain a target language model; sequentially inputting each text in the to-be-clustered corpus into the target language model for feature extraction, obtaining a sentence vector of each text in the to-be-clustered corpus according to a model output result, and generating a to-be-clustered sentence vector set; and, by utilizing a preset clustering algorithm, clustering the to-be-clustered corpora based on the to-be-clustered sentence vector set to obtain sentence vectors corresponding to each category, and determining a clustering result of the to-be-clustered corpora. The invention further discloses an electronic device and a computer storage medium. By utilizing the method and the device, the text clustering accuracy and efficiency can be improved.

The invention discloses a static clustering algorithm for a wireless sensor network, which belongs to the technical field of wireless sensor networks. The algorithm is applied to topology control of the wireless sensor network, and is different from the conventional network clustering algorithm. In the algorithm, clusters are partitioned firstly through convergent nodes, and a current turn of cluster head is used for electing a next turn of cluster head. The executing process of the algorithm is periodical, and is partitioned into a cluster partitioning stage, a cluster head selecting stage and a stable data communication stage when a network is established or a large quantity of nodes are added or leave the network, and only the last two stages are performed in the rest of periods. Compared with the conventional hierarchy topology, the algorithm has the advantages that: energy consumption in a network deployment process can be lowered, hierarchical division is more reasonable, and the service life of the network can be effectively prolonged.

The invention discloses a box separation method based on k-means clustering. The box separation method comprises the following steps that continuous variables are preprocessed; normalization processing is carried out on the preprocessed data, a k-means clustering algorithm is applied on the data obtained after the normalization processing is carried out to divide the data into a plurality of sections; the equal interval method is adopted for setting the initial center of the k-means clustering algorithm to obtain clustering centers; after the clustering centers are obtained, the midpoint of the adjacent clustering centers is used as a classification division point, each object is added into the closest class, and therefore the data are divided into the multiple sections; each clustering center is calculated again, then the data are divided again until each clustering center does not change any more, and the final clustering result is obtained. According to the box separation method, the technical problem that errors are likely to be caused for a data set with the obvious data density distribution bias according to an existing box separation method is solved, the k-means clustering algorithm does not select the initial center randomly any more, and the data separation result is accurate.

The invention relates to a brain MR image segmentation method based on superpixel fuzzy clustering. The method comprises the following steps: 1) obtaining an MR image; 2) performing superpixel segmentation on the MR image and thus a plurality of atom regions are obtained; 3) performing secondary refinement segmentation on the atom regions whose grey value variance is big; 4) performing fuzzy clustering on the atom regions, so that category membership degree of each atom region is obtained; 5) defining the atom regions, whose membership degrees are not clear enough, as fuzzy blocks and realizing affiliation category judgment for the fuzzy blocks through a function iterative method; and 6) performing superpixel merging operation on the atom regions and thus image segmentation results are obtained. The method is a combination of a superpixel method and a fuzzy c-mean clustering algorithm; advantages of the superpixel method and the fuzzy c-mean clustering algorithm are effectively utilized to targetedly overcome the defect that the fuzzy c-mean clustering algorithm is sensitive to noise and a bias field during pixel level clustering; and compared with the traditional fuzzy c-mean clustering algorithm, the method is higher in segmentation accuracy and robustness.

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

The invention discloses an improved k-means clustering method based on a distributed computing platform, and introduces a distributed computing platform Spark for the problem of slow processing of mass data, the Kruskal's algorithm for the problem of too many iterations and the Tanimoto distance for the problem of giving no consideration to the correlation among features of a vector. First, a minimum spanning tree is constructed for the randomly selected k points by using the Kruskal's algorithm, the corresponding weight sum is obtained, and the process is repeated for n times. Then, according to weight sums obtained within the n times, the maximum weight is selected thereform and that distance values between edges composed of the k points are not much different is ensured. In this way, the relatively uniform distribution of cluster centers can be guaranteed. Finally, a clustering algorithm is performed using k-means algorithm improved by using the Tanimoto distance.

The invention discloses a differential privacy protection-oriented k-means clustering method. The K-means clustering method comprises the following steps: performing data preprocessing; ensuring thatC indicates a clustered centered point set, and C indicates a sum of error square of a given data set and a cluster center C; judging the volume of C; performing cyclic execution until retry is greater than a maximum value retrymatx of given retry times, and then returning to the best central point Cbest; traversing each point of the data set X, classifying the points to the nearest central point;setting added random noises; renewedly calculating the sum of the data points of each cluster and the quantity of the points, and adding the noises and finally updating the quality center of the cluster; and repeatedly carrying out the steps until the sum of error square is converged or iteration times reach the upper limit. According to the differential privacy protection-oriented k-means clustering method disclosed by the invention, the appropriate random noises which are specially distributed are added in an iteration process of a k-means clustering algorithm, so that a clustering result is distorted to a certain extent, the aim of privacy protection is fulfilled, and meanwhile, the availability of data is ensured.

The invention discloses a deep learning clustering method for noise images. The deep learning clustering method comprises the following steps: S1, constructing a deep learning clustering model; S2, adopting an AMsoftmax layer as a clustering device, and generating a clustering result according to the feature vector output by the encoder in the step S1; S3, measuring the similarity between the output of the encoder and the output of the twin network by adopting an L2 norm; S4, adopting KL divergence to measure the distribution difference between the clustering result and the auxiliary target distribution; S5, training a deep learning clustering model; And S6, obtaining a clustering result of the data through the AMsoftmax layer. According to the method, unsupervised clustering can be carried out on image data containing noise, and the problems that most image clustering algorithms do not model noise data and an existing deep clustering algorithm is poor in clustering effect on images with high non-linear characteristics are solved.

The invention discloses a thunderstorm kernel identification and tracing method based on a hybrid clustering algorithm. The method specifically includes the following steps of utilizing deployed thunder and lightning monitoring points to conduct exploration and record cloud-to-ground lightning data, conducting preprocessing on the recorded cloud-to-ground lightning data and dividing the data intolightning data sets of each equal time interval; adopting a GPS clock synchronization technology and an algorithm of time differences of arrival for figuring out spatial positioning coordinates of lightning according to the time differences of arrival of changeable radiation pulses of an electric field generated by the lightning to each station; on the basis of thunder and lightning positioning data figured out by means of the procedures above, utilizing a DBSCAN algorithm and a KMEANS algorithm to figure out the relevance of the thunder and lightning positioning data among a thunderstorm kernel center-of-mass coordinate position, the lightning frequency and a thunderstorm kernel. An experimental result indicates that the method can accurately reflect the change tendency of thunder and lightning on thunderstorm days, and great effects of identifying the thunderstorm kernel and movably tracing a thunderstorm are achieved.

The invention discloses a dynamic weighted hybrid clustering algorithm based circuit breaker fault diagnosis method. The method includes the following steps: (1) capturing the energy changes of mechanical drive during the operation of a circuit breaker by utilizing three-axis vibration and two-way sound signals, decomposing the signals through local mean, and extracting the approximate entropy ofeach PF component as the characteristic quantity of a circuit breaker vibration signal; (2) optimizing the initialized clustering center of fuzzy kernel clustering by utilizing the maximum density peak decision of a density peak clustering algorithm, and considering different influences of different characteristics and different samples on clustering results; (3) performing checking on a clustering number K through a cluster validity index MIA; (4) inputting correctly classified characteristics into a multi-level classifier of a support vector machine to perform training; and (5) finding the optimal parameter of the support vector machine through mesh generation, and inputting test data samples to perform final fault classification prediction so that classification accuracy rates can be obtained;. The method has advantaged of being fast in fault diagnosis speed and high in accuracy rate.

The present invention provides a swarm intelligence based behavior clustering system. The data representation of the system comprises a data structure and a data type, and a k-means mixed clustering algorithm is employed; the k-means mixed clustering algorithm which combines an ant colony clustering algorithm and a k-means clustering algorithm is employed and is mainly divided into two parts, a first part is used for carrying out ant colony clustering, a second part is used for collecting an ant colony clustering result by using a k-means algorithm; in the k-means mixed clustering algorithm, a similarity formula is similar to a fundamental model for ant colony clustering and an LF algorithm, but simpler probability transformation functions are adopted and are of two straight lines with the slope of k; and proved by subsequent experiments, due to improvements, both the precision and efficiency of the algorithm are more excellent compared with the existing algorithms.

The invention discloses a pavement crack recognition algorithm method and system based on dual-scale clustering algorithm. The pavement crack recognition algorithm method includes steps of reading a three-dimensional image data matrix to obtain a binary image by a computer; scanning a data matrix corresponding to the binary image to obtain a marked crack area by an eight-neighborhood search algorithm according to the sequence from top to bottom, left to right; clustering cracks by the dual-scale clustering algorithm to obtain the clustered crack area; utilizing the minimum circumscribed ellipse where the clustered crack area is located as pavement cracks. The pavement crack recognition algorithm method is low in complexity, short in operation time and free of manual involvement. Disordered crack data is locally linearly fitted, model-established theory is indicated by a regular and certain mathematical expression, and data processing complexity is lowered; detection of the pavement cracks can be completed by inputting and acquiring pavement crack data only, so that the algorithm is high in detecting efficiency and fast and has a certain research value.

The invention provides a tag-co-occurred tag clustering method. A tag matrix, a common tag matrix, a tag importance degree matrix and a similarity matrix are defined in order to improve clustering effectiveness; feature vectors of tags are determined by extracting tag co-occurrence information; the method that the distance between one object and another object is calculated by using geometrical distance in the traditional clustering algorithm is changed into the method by using Pearson correlation coefficient in the way that the similarity is calculated by extracting the feature vectors; the tag-co-occurred tag clustering method which is used for clustering the tags by being combined with the K-means clustering algorithm is provided. The clustering method provided by the invention is better than the other clustering methods in effect and has good effectiveness and feasibility.

The invention discloses an SAR image segmentation method based on a manifold distance two-stage clustering algorithm. The method mainly solves the problem that an existing clustering segmentation algorithm is unstable in result and big in calculated amount. Achieving steps includes: (1) setting an ending condition and an operation parameter; (2) inputting an image to be segmented, and conducting pre-segmentation on the image to be segmented; (3) extracting characteristics of an image block obtained by pre-segmentation; (4) taking Euclidean distance to be used as similarity measurement to conduct first stage clustering for the characteristics of the image block; (5) taking a clustering center of a first stage and a point farthest from the center as representative points; (6) calculating a manifold distance between any two representative points; (7) taking the manifold distance as the similarity measurement to conduct clustering of a second stage of the representative points; (8) refreshing a clustering center; and (9) judging whether an end condition is achieved, if the end condition is not achieved, returning the step (7), or outputting a segmentation result image. The method has the advantages of being accurate in segmentation result, stable, short in time, and capable of being used in the technical fields such as image strengthening, pattern recognition and target tracking.