NDLI: Discovering frequent geometric subgraphs

Content Provider	IEEE Xplore Digital Library
Author	Kuramochi, M. Karypis, G.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci., Minnesota Univ., Minneapolis, MN, USA (Kuramochi, M.; Karypis, G.)
Abstract	As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. We present a computationally efficient algorithm for finding frequent geometric subgraphs in a large collection of geometric graphs. Our algorithm is able to discover geometric subgraphs that can be rotation, scaling and translation invariant, and it can accommodate inherent errors on the coordinates of the vertices. Our experimental results show that our algorithms require relatively little time, can accommodate low support values, and scale linearly on the number of transactions.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Pattern Analysis & Machine Intelligence (TCPAMI) IEEE Comput. Soc. Tech. Committee on Computational Intelligence (TCCI)
Starting Page	258
Ending Page	265
File Size	598609
Page Count	8
File Format	PDF
ISBN	0769517544
DOI	10.1109/ICDM.2002.1183911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-12-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spatial databases Transaction databases Data mining Computer science Itemsets US Department of Energy High performance computing Contracts Military computing Chemical compounds
Content Type	Text
Resource Type	Article

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