Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Applied multivariate techniques
Applied multivariate techniques
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
An evaluation of criteria for measuring the quality of clusters
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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The goal of any clustering algorithm producing flat partitions of data, is to find both the optimal clustering solution and the optimal number of clusters. One natural way to reach this goal without the need for parameters, is to involve a validity index in a clustering process, which can lead to an objective selection of the optimal number of clusters. In this paper, we provide an evaluation of the major relative indices involving them in an agglomerative clustering algorithm for documents. The evaluation seeks the indices ability to identify both the optimal solution and the optimal number of clusters. Then, we propose a new context-aware method that aims at enhancing the validity indices usage as stopping criteria in agglomerative algorithms. Experimental results show that the method is a step-forward in using, with more reliability, validity indices as stopping criteria.