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Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by...
Biological networks having complex connectivity have been widely studied recently. By characterizing their inherent and structural behaviors in a topological perspective, these studies have attempted to discover hidden knowledge in the systems. However, even though various algorithms with graph-theoretical modeling have provided fundamentals in the network analysis, the availability of practical approaches...
Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics, and an offline phase that generates the clusters by using the summary data. In this paper we propose a data stream clustering method based on a multi-agent system that uses a decentralized bottom-up self-organizing strategy...
A novel dynamic evolutionary clustering algorithm (DECA) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. DECA searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes; at the same time, the convergence of clustering...
It has been shown that the use of a reciprocation mechanism in peer-to-peer grid systems which provide multiple services to their users is an efficient way to prevent free-riding and, at the same time, to promote the clustering of peers that have mutually profitable interactions. However, when peers are subject to resource limitations, they may be unable to offer all possible services and shall select...
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