NDLI: Enhanced co-occurrence distances for categorical data in unsupervised learning

Content Provider	IEEE Xplore Digital Library
Author	Jia-Yi Feng Ming-Chun Wang Can Wang Long-Bing Cao
Copyright Year	2010
Description	Author affiliation: Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia (Can Wang; Long-Bing Cao) \|\| Department of science, Tianjin University of Technology and Education, Tianjin 300222, China (Jia-Yi Feng; Ming-Chun Wang)
Abstract	Distance metrics for categorical data play an important role in unsupervised learning such as clustering. They also dramatically affect learning accuracy and computational complexities. Recently, two co-occurrence methods, Co-occurrence Distance based on Power Set (CDPS) and Co-occurrence Distance based on Universal Set (CDUS), have been proposed to calculate distances for categorical attribute values with significantly improved clustering accuracy by taking advantage of co-occurrences of attributes. However, their computational load is high enough to restrict their applications in unsupervised learning. This paper proposes two new enhanced co-occurrence approaches, i.e. Co-occurrence Distance based on Join Set (CDJS) and Co-occurrence Distance based on Intersection Set (CDIS), to calculate the distance between two values of a categorical attribute by considering its relationships to other attributes. Theoretical analysis reveals the equivalent accuracy of CDJS and CDIS to CDPS and CDUS, while CDJS and CDIS can significantly reduce computational complexity. Substantial experiments on ten benchmark and real-world data sets have evidenced that our proposed approaches are equivalently accurate but with a much higher efficiency than CDPS and CDUS, in particular for large scale data sets.
Starting Page	2071
Ending Page	2078
File Size	206330
Page Count	8
File Format	PDF
ISBN	9781424465262
e-ISBN	9781424465279
DOI	10.1109/ICMLC.2010.5580500
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Accuracy Computational complexity Unsupervised learning Iterative closest point algorithm Machine learning Cybernetics clustering Co-occurrence distances Categorical data
Content Type	Text
Resource Type	Article

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