Similarity Measures for Multi-valued Attributes for Database Clustering

This paper introduces an approach to cope with the representational inappropriateness of traditional flat file format for data sets from databases, specifically in database clustering. After analyzing the problems of the traditional flat file format to represent related information, a better representation scheme called extended data set that allows attributes of an object to have multi-values is introduced, and it is demonstrated how this representation scheme can represent structural information in databases for clustering. A unified similarity measure framework for mixed types of multi-valued and single-valued attributes is proposed. A query discovery system, MASSON that takes each cluster is used to discover a set of queries that represent discriminant characteristic knowledge for each cluster. INTRODUCTION Many data analysis and data mining tools, such as clustering tools, inductive learning tools, statistical analysis tools, assume that data sets to be analyzed are represented as a single flat file (or table) in which an object is characterized by attributes that have a single value. Person Purchase Joined result