Abstract
In this paper we present a new approach for the discovery of meaningful clusters from large categorical data (which is an usual situation, e.g., web data analysis). Our method called ECCLAT (for Extraction of Clusters from Concepts LATtice) extracts a subset of concepts from the frequent closed itemsets lattice, using an evaluation measure. ECCLAT is generic because it allows to build approximate clustering and discover meaningful clusters with slight overlapping. The approach is illustrated on a classical data set and on web data analysis.
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Durand, N., Crémilleux, B. (2003). ECCLAT: a New Approach of Clusters Discovery in Categorical Data. In: Bramer, M., Preece, A., Coenen, F. (eds) Research and Development in Intelligent Systems XIX. Springer, London. https://doi.org/10.1007/978-1-4471-0651-7_13
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DOI: https://doi.org/10.1007/978-1-4471-0651-7_13
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