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In a large scale sensor network for traffic surveillance, the data to be transmitted is huge, which leads to high cost of communication. When sensor nodes are connected wirelessly, the situation will be worse. So it's necessary to reduce the data amount before data packets are transmitted. In this paper, we propose a distributed algorithm base on FCM (fuzzy c-means clustering). Parameters in the algorithm...
Biclustering is an important data mining technique that allows identifying groups of genes which behave similarly under a subset of conditions for analyzing gene expression data from microarray technology. As a gene may play more than one biological role in conjunction with distinct groups of genes, possibilistic biclustering algorithms can give much insight towards different biological processes...
Horizontal collaborative clustering is such a clustering method that carries clustering on a pattern set described in one feature space with collaborative introducing outer clustering information obtained by clustering the same pattern set but described in some other different feature spaces. For the sake of privacy-preserving, the outer clustering information is usually provided by the outer partition...
As a typical extension of fuzzy c-means, horizontal collaborative fuzzy clustering implements the clustering on a data set of some patterns with the collaboration of some knowledge which is obtained from other data set(s) about the same patterns but described in different feature space(s). For the sake of safety and personal privacy, the knowledge is usually provided by partition matrixes. This paper...
This paper first gives a new validity function for fuzzy clustering, then presents a method of the optimal selecting of the cluster number in the standard fuzzy c-means clustering algorithm, and finally outlines the fuzzy c-means clustering algorithm with parameters self-adapted. Experimental results carried on synthetic data set and data set based on actual background illustrate the performance of...
In many applications of document clustering, a document may include multiple topics and thus may relate to multiple categories at the same time. Most of the existing subspace clustering algorithms can only perform hard clustering on document collections. In this paper, a fuzzy algorithm named R-FPC is introduced for document clustering. The algorithm discovers soft partitions of a data set in the...
Because Fuzzy c-means (FCM) clustering algorithm has the problems of initializing the cluster centers and a huge number of computing in the iteration, this paper presents an improved method. It can optimize the data set to reduce the time for each of iteration, and then use cluster centers obtained by the sample density as the initial cluster centers to reduce the number of iterations required for...
In machine learning and pattern recognition fields, collecting labeled examples costs human efforts, while vast amounts of unlabelled data are often readily available and offer some additional information. In practice, many applications require a dimensionality reduction method to deal with the partially labeled problem. In this paper, we propose a semi-supervised clustering framework, which is based...
Image segmentation algorithm based on fuzzy c-means clustering is an important algorithm in the image segmentation field. It has been used widely. However, it is not successfully to segment the noise image because the algorithm disregards of special constraint information. It only considers the gray information. Therefore, we proposed a weighed FCM algorithm based on Gaussian kernel function for image...
Time duration and presence of a Web page are two factors disclosing Web users' interest. The time duration on a web page is characterized as a fuzzy linguistic variable because it is easily understandable for people and the subtle difference between two durations is disregarded. Thus a Web access pattern is transformed as a fuzzy Web access pattern, which is a fuzzy vector that are composed of n fuzzy...
According to the high-dimensional sparse features of the storage of the textual document, and defects existing in the clustering methods which have already studied by now and some other problems, an effective text clustering approach (short for TGSOM-FS-FKM) based on tree-structured growing self-organizing maps (TGSOM) and fuzzy k-means (FKM) is proposed. It firstly makes preprocess of texts, and...
Fuzzy clustering is a popular method for modeling web usage data, and a number of techniques have been proposed. Performance of such techniques has been demonstrated through experiments using datasets which are often limited in the size and/or variety. This is mainly due to the difficulty in acquiring large real data, and also to the huge amount of time and effort required in performing experiments...
The clustering capabilities of the Non Negative Matrix Factorization algorithm is studied. The basis images are considered like the membership degree of the data to a particular class. A hard clustering algorithm is easily derived based on these images. This algorithm is applied on a multivariate image to perform image segmentation. The results are compared with those obtained by Fuzzy K-means algorithm...
In complex process of industrial production, it need deal with a large number of data, multiple dimensions, and generate complex data. If the neural network control indirect used, it is easy that lead to some shortcomings, such as inaccurate results and training stage of neural network lack convergence and so forth. In response to these circumstances, the integration model of data optimize processing...
Clustering high dimensional data is a big challenge in data mining due to the curse of dimensionality. To solve this problem, projective clustering has been defined as an extension of traditional clustering that seeks to find projected clusters in subsets of dimensions of a data space. In this paper, the problem of modeling projected clusters is first discussed, and an extended Gaussian model is proposed...
The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO...
Web mining is defined as applying data mining techniques to the content, structure, and usage of Web resources. The three areas of Web mining are commonly distinguished: content mining, structure mining, and usage mining. In all these areas, a wide range of general data mining techniques, in particular association rule discovery, clustering, classification, and sequence mining, are employed and developed...
Clustering is generally done on individual object data representing the entities such as feature vectors or on object relational data incorporated in a proximity matrix.This paper describes another method for finding a fuzzy membership matrix that provides cluster membership values for all the objects based strictly on the proximity matrix. This is a form of relational data clustering. The fuzzy membership...
In this paper, two new clustering algorithms are proposed for data with some errors. In any of these algorithms, the error is interpreted as one of decision variables - called ldquotolerancerdquo - of a certain optimization problem like the previously proposed algorithm, but the tolerance in new methods is determined by the new introduced penalty term of it in the corresponding objective function...
The proposed relational fuzzy clustering method called FRFP (fuzzy relational fixed point) is not based on minimizing an objective function, as in traditional methods, but rather on determining a fixed point of a function of the desired membership matrix with the proximity matrix as parameter. The proposed method is compared to other relational clustering methods including NERFCM, Rouben's method...
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