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Representative-based clustering algorithms are quite popular due to their relative high speed and because of their sound theoretical foundation. On the other hand, the clusters they can obtain are limited to convex shapes and clustering results are also highly sensitive to initializations. In this paper, a novel agglomerative clustering algorithm called MOSAIC is proposed which greedily merges neighboring...
Clustering is a very important problem that has been addressed in many contexts and by researchers in many disciplines. This paper presents a new stochastic nature inspired methodology, which is based on the concepts of Particle Swarm Optimization (PSO) and Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm (Hybrid PSO-GRASP)...
The datasets extracted from large retail stores often contain sparse information composed of a huge number of items and transactions, with each transaction only containing a few items. These data render basket analysis with extremely low item support, customer clustering with large intra cluster distance and transaction classifications having huge classification trees. Although a similarity measure...
Clustering is generally defined as an unsupervised data mining process which aims to divide a set of data into groups, or clusters, such that the data within the same group are similar to each other while data from different groups are dissimilar. However, additional background information (namely constraints) are available in some domains and must be considered in the clustering solutions. Recently,...
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