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Content Provider | IEEE Xplore Digital Library |
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Author | Spyropoulou, E. De Bie, T. |
Copyright Year | 2011 |
Abstract | Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for highly simplified types of data, such as an attribute-value table or a binary database, such that those methods are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-relational data. We propose a new syntax for multi-relational patterns as complete connected sub graphs in a representation of the database as a k-partite graph. We show how this pattern syntax is generally applicable to multirelational data, while it reduces to well-known tiles [7] when the data is a simple binary or attribute-value table. We propose RMiner, an efficient algorithm to mine such patterns, and we introduce a method for quantifying their interestingness when contrasted with prior information of the data miner. Finally, we illustrate the usefulness of our approach by discussing results on real-world and synthetic databases. |
Starting Page | 675 |
Ending Page | 684 |
File Size | 631513 |
Page Count | 10 |
File Format | |
ISBN | 9781457720758 |
ISSN | 15504786 |
DOI | 10.1109/ICDM.2011.82 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-12-11 |
Publisher Place | Canada |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Data mining Entropy Motion pictures Syntactics Itemsets Bipartite graph |
Content Type | Text |
Resource Type | Article |
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