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Detecting significant distinguishing sets among bi-clusters

Published: 26 October 2008 Publication History

Abstract

A fundamental task of data analysis is comprehending what distinguishes clusters found within the data. We present the problem of mining distinguishing sets; which seeks to find sets of objects or attributes that induce the most incremental change between adjacent bi-clusters of a binary dataset. Viewing the lattice of bi-clusters formed within a data set as a weighted directed graph, we mine the most significant distinguishing sets by growing a maximal-cost spanning tree of the lattice.

References

[1]
Madeira S. C. and Oliveria A. L. BiclusteringAlgorithms for Biological Data Analysis: A Survey. IIEEE Transactions on Computational Biology and Bioinformatics vol. 1, no.1 pp. 24--45, 2004.
[2]
Shiram Narayanswami and Raj Bhatnagar. A Lattice-Based Model for Recommeder Systems. To appear in the proceedings of the ICTAI-2008 (November) Conference.
[3]
B. Ganter, and R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer-Verlag, Heidelberg, 1999.
[4]
Mohammed J. Zaki and Ching-Jui Hsiao. Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure. IEEE Transactions on Knowledge and Data Engineering vol. 17, no. 4, 2005.
[5]
Christian Lindig. Fast Concept Analysis. 8th International Conference on Conceptual Structures, 2000.

Cited By

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  • (2012)BibliographyContrast Data Mining10.1201/b12986-34Online publication date: 10-Oct-2012
  • (2010)A novel framework for detecting maximally banded matrices in binary dataStatistical Analysis and Data Mining10.1002/sam.100893:6(431-445)Online publication date: 7-Sep-2010

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    cover image ACM Conferences
    CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management
    October 2008
    1562 pages
    ISBN:9781595939913
    DOI:10.1145/1458082
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 26 October 2008

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    Author Tags

    1. bi-clustering
    2. formal concept analysis

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    CIKM08
    CIKM08: Conference on Information and Knowledge Management
    October 26 - 30, 2008
    California, Napa Valley, USA

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    View all
    • (2012)BibliographyContrast Data Mining10.1201/b12986-34Online publication date: 10-Oct-2012
    • (2010)A novel framework for detecting maximally banded matrices in binary dataStatistical Analysis and Data Mining10.1002/sam.100893:6(431-445)Online publication date: 7-Sep-2010

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