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A Database Clustering Methodology and Tool by Tae-Wan Ryu; Christoph F. Eick is a Computer Science article available to read on EtoBox.

What is A Database Clustering Methodology and Tool about?

Clustering is a popular data analysis and data mining technique. However, applying traditional clustering algorithms directly to a database is not straightforward due to the fact that a database usually consists of structured and related data; moreover, there might be several object views of the database to be clustered, depending on a data analyst's particular interest. Finally, in many cases, there is a data model discrepancy between the format used to store the database to be analyzed and the representation format that clustering algorithms expect as their input. These discrepancies have been mostly ignored by current research. This paper focuses on identifying those discrepancies and on analyzing their impact on the application of clustering techniques to databases. We are particularly interested in the question on how clustering algorithms can be generalized to become more directly applicable to real-world databases. The paper introduces methodologies, techniques, and tools that serve this purpose. We propose a data set representation framework for database clustering that characterizes objects to be clustered through sets of tuples, and introduce preprocessing techniques and

Who reads A Database Clustering Methodology and Tool?

Author

Tae-Wan Ryu; Christoph F. Eick

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0020-0255)

Published

2005

Language

EN

Field

Computer Science (Physical Sciences)