NDLI: Finding low-entropy sets and trees from binary data

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Finding low-entropy sets and trees from binary data

Content Provider	ACM Digital Library
Author	Hinkkanen, Eino Mannila, Heikki Mielikäinen, Taneli Heikinheimo, Hannes Seppänen, Jouni K.
Abstract	The discovery of subsets with special properties from binary data hasbeen one of the key themes in pattern discovery. Pattern classes suchas frequent itemsets stress the co-occurrence of the value 1 in the data. While this choice makes sense in the context of sparse binary data, it disregards potentially interesting subsets of attributes that have some other type of dependency structure. We consider the problem of finding all subsets of attributes that have low complexity. The complexity is measured by either the entropy of the projection of the data on the subset, or the entropy of the data for the subset when modeled using a Bayesian tree, with downward or upward pointing edges. We show that the entropy measure on sets has a monotonicity property, and thus a levelwise approach can find all low-entropy itemsets. We also show that the tree-based measures are bounded above by the entropy of the corresponding itemset, allowing similar algorithms to be used for finding low-entropy trees. We describe algorithms for finding all subsets satisfying an entropy condition. We give an extensive empirical evaluation of the performance of the methods both on synthetic and on real data. We also discuss the search for high-entropy subsets and the computation of the Vapnik-Chervonenkis dimension of the data.
Starting Page	350
Ending Page	359
Page Count	10
File Format	PDF QT / MOV
ISBN	9781595936097
DOI	10.1145/1281192.1281232
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Local models Pattern discovery
Content Type	Video Text
Resource Type	Article

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