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Evolutionary, Neural, and Statistical Approaches to Interval Clustering for Web Mining

Rough set theory has been used extensively for supervised learning. Clustering within the context of rough set theory is attracting increasing interest among researchers. Typical clustering operations in data mining involve finding natural groupings of resources or users. Conventional clusters have crisp boundaries, i.e. each object belongs to only one cluster. The clusters and associations in data mining do not necessarily have crisp boundaries. An object may belong to more than one cluster. This paper describes three different methodologies based on properties of rough sets for developing interval representations of clusters. The first approach is based on Genetic Algorithms, the second approach is an adaptation of the K-means algorithm, and the last is based on Kohonen self-organizing maps. The paper also provides an experiment to illustrate the rough set based clustering of web users.