Object aggregation and cluster identification: a knowledge discovery approach

Abstract A method for object aggregation and cluster identification has been proposed for knowledge discovery in databases. By integrating conceptual clustering and machine learning (especially learning-from-examples) paradigms, the method classifies the data into different clusters, extracts the characteristics of each cluster, and discoversknowledge rules based on the relationships among different clusters. Different kinds of knowledge rules, including hierarchical, equivalence and inheritance rules can be discovered efficiently.

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