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FuzzyShrinking: improving shrinking-based data mining algorithms using fuzzy concept for multi-dimensional data

Published: 28 March 2008 Publication History

Abstract

In this paper, we present continuous research on data analysis based on our previous work on the shrinking approach. Shrinking[19] is a novel data preprocessing technique which optimizes the inner structure of data inspired by the Newton's Universal Law of Gravitation[16]in the real world. It can be applied in many data mining fields. In this approach data are moved along the direction of the density gradient, thus making the inner structure of data more prominent. It is conducted on a sequence of grids with different cell sizes. In this paper, we applied the Fuzzy concept to improve the performance of the shrinking approach, targeting the better decision making for the movement for individual data points in each iteration. This approach can assist to improve the performance of existing data analysis approaches.

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ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference
March 2008
548 pages
ISBN:9781605581057
DOI:10.1145/1593105
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 March 2008

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ACM SE08
ACM SE08: ACM Southeast Regional Conference
March 28 - 29, 2008
Alabama, Auburn

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