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Information Technology Journal
  Year: 2009 | Volume: 8 | Issue: 2 | Page No.: 173-180
DOI: 10.3923/itj.2009.173.180
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Integrated Approach of Reduct and Clustering for Mining Patterns from Clusters

A. Arora, S. Upadhyaya and R. Jain

In this study, a method is presented for selection and ranking of significant attributes for individual clusters which lead to formulation of concise and user understandable patterns. Cluster is set of similar data objects and similarity is measured on attribute values. Attributes which have same value for majority of objects in a cluster are considered significant and rest non significant for that cluster. Reduct from rough set theory is defined as the set of attributes which distinguishes the objects in a homogenous cluster, therefore these can be clear cut removed from the same. Non reduct attributes are ranked for their contribution in the cluster. Pattern is then formed by conjunction of most contributing attributes of that cluster.
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  •    An Intelligent Mining Framework based on Rough Sets for Clustering Gene Expression Data
  •    A Fast Evolutionary Algorithm for Automatic Evolution of Clusters
  •    Anomaly Detection in Transactional Sequential Data
  •    Efficient Clustering for High Dimensional Data: Subspace Based Clustering and Density Based Clustering
How to cite this article:

A. Arora, S. Upadhyaya and R. Jain, 2009. Integrated Approach of Reduct and Clustering for Mining Patterns from Clusters. Information Technology Journal, 8: 173-180.

DOI: 10.3923/itj.2009.173.180






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