Publication
Title
Cartification : a neighborhood preserving transformation for mining high dimensional data
Author
Abstract
The analysis of high dimensional data comes with many intrinsic challenges. In particular, cluster structures become increasingly hard to detect when the data includes dimensions irrelevant to the individual clusters. With increasing dimensionality, distances between pairs of objects become very similar, and hence, meaningless for knowledge discovery. In this paper we propose Cartification, a new transformation to circumvent this problem. We transform each object into an item set, which represents the neighborhood of the object. We do this for multiple views on the data, resulting in multiple neighborhoods per object. This transformation enables us to preserve the essential pair wise-similarities of objects over multiple views, and hence, to improve knowledge discovery in high dimensional data. Our experiments show that frequent item set mining on the certified data outperforms competing clustering approaches on the original data space, including traditional clustering, random projections, principle component analysis, subspace clustering, and clustering ensemble.
Language
English
Source (book)
Data Mining (ICDM) : 2013 IEEE 13th International Conference on Data Mining, 7-10 December 2013, Dallas, Texas, USA
Publication
New York, N.Y. : IEEE, 2013
ISSN
1550-4786
Volume/pages
p. 937-942
ISI
000332874200095
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 03.03.2014
Last edited 19.04.2017
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