NDLI: Mining Discrete Patterns via Binary Matrix Factorization

Content Provider	IEEE Xplore Digital Library
Author	Peng Jiang Heath, M.T.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Peng Jiang; Heath, M.T.)
Abstract	In general, binary matrix factorization (BMF) refers to the problem of finding two binary matrices of low rank such that the difference between their matrix product and a given binary matrix is minimal. BMF is an important tool in mining discrete patterns for high-dimensional data. One approximate matrix factor finds the dominant patterns, and the other shows how the original patterns are represented by the dominant ones. The problem of determining the exact optimal solution is NP-hard. We show that BMF is closely related with k-means clustering and propose a clustering approach for BMF. We prove that our approach has approximation ratio of 2. We further propose a randomized clustering algorithm that chooses k cluster centroids randomly based on preassigned probabilities to each point. The randomized clustering algorithm works well for large k. We experimentally demonstrate the nice theoretical properties of BMF on applications in pattern extraction and association rule mining.
Sponsorship	Toshiba
Starting Page	1129
Ending Page	1136
File Size	412994
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.46
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	k-means clustering pattern extraction Clustering algorithms Binary matrix factorization approximation algorithm Approximation algorithms Vectors Partitioning algorithms Approximation methods Data mining Matrix decomposition association rule mining
Content Type	Text
Resource Type	Article

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