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Abstract: This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation. Subsequent clusters are formed using the concept of constrained-similarity upper approximation wherein a condition of relative similarity is used as a merging criterion. We report results of experimentation on msnbc web navigation dataset that are intrinsically sequential in nature. We have compared the results of the proposed approach with that of the traditional hierarchical clustering algorithm using vector coding of sequences. The results establish the viability of the proposed approach. The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group. Such descriptions can help web miners to identify potential and meaningful groups of users. [Copyright &y& Elsevier]

Data mining is widely used to solve real-world problems in engineering, science and business. Usually the results from data mining obtained through the traditional approaches are not interpretable in the real life scenario. This paper suggests an approach for logical interpretations of the clustered data. Our approach involves using fuzzy ART technique for clustering the data and then applying the soft regression technique for interpreting the results of the clustering. The proposed model provides better analysis of data for describing overlapping clusters. We used our model to analyze patterns in the advances data of a public sector bank. Analyses and experiments show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]