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This paper presents a fast heuristic which finds clusters by partitioning categorical large data sets according to the Relational Analysis, whereby the cluster analysis is modeled as a linear integer program with n2 attributes (n is the number of observations) and solved by the optimization under constraints of the Condorcet criterion. Without neither a sampling method nor the fixing of input parameters...
Finding out the number of clusters for a particular problem statement is quite an important aspect in clustering. Many clustering techniques have been experimented in the recent past providing somewhat satisfactory results. User supplied information as well as cluster validity indices are some of the expensive techniques regarding to the computation time. A novel technique has been proposed using...
With the advent of modern techniques for scientific data collection, large quantities of data are getting accumulated at various databases. Systematic data analysis methods are necessary to extract useful information from rapidly growing data banks. Cluster analysis is one of the major data mining methods and the k-means clustering algorithm is widely used for many practical applications. But the...
During the last few years, the search result clustering has attracted a substantial amount of research. In this paper, we present a comparative study of the performance of fuzzy clustering algorithms, namely Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms with clustering search results. Therefore, there is a need to reduce the information, help filtering out irrelevant items, and favors...
The traditional clustering algorithms are only suitable for the static datasets. As for the dynamic and incremental datasets, the clustering results will become unreliable after data updates, and also it will certainly decrease efficiency and waste computing resources to cluster all of the data again. To overcome these problems, a new incremental clustering algorithm is proposed on the basis of density...
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