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The density at a data point is defined based on kernel function. And we introduce weight to refine rough k-means algorithm. Then we construct the formula for calculating local outlier score based on the clusters generated by the refined rough k-means algorithm. We use a synthetic data set and a real-world data set to verify that the new technique for local outliers detection is not only accurate but...
This paper mainly studies the complex network detection algorithm, and improves an algorithm based on K-means, Another reference node density properties, this paper puts forward a method community structure detection algorithms (BSTN) based on similarity between the nodes of the complex network, the algorithm greatly reduce iteration times, using the algorithm in the computer generated stochastic...
When lines in a power system are constrained, the sensitivity of the power flows on these lines to generator output provides information about how the constraints divide the system and about the ability of sets of generators to increase revenue without increasing dispatch. Clustering is used to identify generators into groups with the potential for market advantage. In this paper, we discuss the implementation...
A clustering problem with balancing constraints is studied in this paper, which means that the sample number in each cluster has to be at least pre-given value. A modified k-means clustering algorithm is proposed, which adopt the proposed heuristic cluster assignment algorithm to deal with the balancing constraints. Numerical computation shows that the proposed algorithm can deal with the balancing...
In content-based multimedia information retrieval, image and video information are usually described by high-dimensional vector, if using a linear scanning approach to search for those feature vectors will undoubtedly increase the cost of calculating, so the approximate vector indexing method and clustering method are used in conjunction in this article to make use of high-dimensional indexing techniques...
K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as `brute force' since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient...
Spectral clustering algorithm is an increasingly popular data clustering method, which derives from spectral graph theory. Spectral clustering builds the affinity matrix of the dataset, and solves eigenvalue decomposition of matrix to get the low dimensional embedding of data for later cluster. A semi-supervised spectral clustering algorithm makes use of the prior knowledge in the dataset, which improves...
Due to the difficulty of determining node number of hidden layers and center, slow learning speed and weaken generalization ability of RBF neural network when input data is generous and complex. A new method based on combined clustering is presented here to determine node number of hidden layer and centers of RBF neural network self-adaptively. In this paper, subtractive clustering is used to cluster...
In the context of unsupervised clustering, a new algorithm for the domain of graphs is introduced. In this paper, the key idea is to adapt the mean-shift clustering and its variants proposed for the domain of feature vectors to graph clustering. These algorithms have been applied successfully in image analysis and computer vision domains. The proposed algorithm works in an iterative manner by shifting...
The Particle Swarm Clustering (PSC) algorithm uses collective intelligence to solve clustering problems. It simulates the interaction of individuals, which use their own experience (cognitive term), social experience (social term) and interaction with the environment (self-organizing term) to cluster objects in different groups. In this work a study of the behavior of particles and an analysis of...
Single pass fuzzy c-means and Online fuzzy c-means are two scalable versions of the widely used fuzzy c-means clustering algorithm. They both facilitate scaling to very large numbers of examples while providing partitions that very closely approximate those one would obtain using fuzzy c-means. They have been successfully applied to a number of data sets, most notably magnetic resonance image volumes...
With the assistance of the lower and upper approximation of rough sets, the rough fuzzy k-means clustering algorithm may improve the objective function and further the distribution of membership function for the traditional fuzzy k-means clustering. However, the algorithm only has theoretical ideas rather than concrete realizations. To make it better applied to practice, using Matlab, a mathematical...
Given a graph G=(V,E) with real positive edge weights, where each edge (u, v) is labeled either + or - depending on whether u and v have been deemed to be similar or dissimilar, the problem of correlation clustering with l clusters is to partition the vertices of G into at most l clusters to minimize the total weight of + edges between clusters and - edges within clusters. This problem for general...
The clusters tend to have vague or imprecise boundaries in some fields such as web mining, since clustering has been widely used. Decision-theoretic rough set model (DTRSM) is a typical probabilistic rough set model, which has the ability to deal with imprecise, uncertain, and vague information. Therefore, a novel clustering algorithm based on the DTRSM is proposed in this paper, which can decide...
Huge amounts of data are available in large-scale networks of autonomous data sources dispersed over a wide area. Data mining is an essential technology for obtaining hidden and valuable knowledge from these networked data sources. In this paper, we investigate clustering, one of the most important data mining tasks, in one of such networked computing environments, i.e., Peer-to-Peer (P2P) network...
This paper presents a hybrid clustering algorithm based on density and ant colony algorithm, that to determine the initial cluster centers according to cluster objects distribution density method, and then use the swarm intelligence and randomness of ant colony algorithm to find that arbitrary shape of clusters, to avoid falling into local convergence, to get a relatively stable global optimal solution...
Centre-based clustering is among the most applicable method for partitioning objects into homogenous groups. This paper presents two Centre-based clustering; K-Means and K-Modes algorithms to investigate and evaluate the clustering results of Y-STR data. The main goal of this paper is to compare the accuracy of clustering Y-STR results for different types of data: numerical and categorical data. The...
This paper studies the extension of the Modularity measure for categorical data clustering. It first shows the relational data presentation and establishes the relationship between the extended Modularity and the Relational Analysis criterion. Two extensions are presented in this work: the early integration and the intermediate integration approaches. The proposed Modularity measure introduces an...
In recent years, research on dictionary design for sparse representation (SR) has changed from pre-defined to training. A Hierarchical K-means Clustering (HKC) dictionary training algorithm is proposed in this paper. The algorithm presents a framework for SR for a class of images. HKC used K-means clustering to generate atoms which is one to one corresponding to hyperplanes for approximating hyperspherical...
TSP arises in many applications such as transportation, manufacturing and various logistics and scheduling. This problem has caught much attention of mathematicians and computer scientists. A clustering strategy will decompose TSP into subgraphs and form clusters, so it may reduce problem size into smaller problem. The primary objective of this research is to produce a better clustering strategy that...
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