Finding Small Sets of Essential Attributes in Binary Data

Authors:
Boros Endre;Horiyama Takashi;Ibaraki Toshihide;Makino Kazuhisa;Yagiura Mutsunori
Affiliations:
-;-;-;-;-
Venue:
Finding Small Sets of Essential Attributes in Binary Data
Year:
2000

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Capturing human intelligence in the net

Communications of the ACM
Finding Essential Attributes in Binary Data

IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents

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Abstract

We consider the problem of finding support sets (i.e., sets of essential attributes) in a given data set, which consists of n-dimensional binary vectors of positive examples and negative examples. A set of attributes is a support set if positive examples and negative examples can be separated by using only the attributes in the set. Finding small support sets is an important topic in such fields as knowledge discovery, data mining, learning theory and logical analysis of data. Based on several measures of separation, we discuss why finding small support sets is important, and how to find such sets, together with results of some computational experiment. Theoretical analysis of the approximation ratios of the proposed algorithms is also provided.