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Pattern discovery for large mixed-mode database

Authors:

Andrew K.C. Wong,

Keith C.C. ChanAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 859 - 868

https://doi.org/10.1145/1871437.1871547

Published: 26 October 2010 Publication History

Abstract

In business and industry today, large databases with mixed data types (continuous and categorical) are very common. There are great needs to discover patterns from them for knowledge interpretation and understanding. In the past, for classification, this problem is solved as a discrete data problem by first discretizing the continuous data based on the class-attribute interdependence relationship. However, so far no proper solution exists when class information is unavailable. Hence, important pattern post-processing tasks such as pattern clustering and summarization cannot be applied to mixed-mode data. This paper presents a new method for solving the problem. It is based on two essential concepts. (1) Though class information is absent, yet for a correlated dataset, the attribute with the strongest interdependence with others in the group can be used to drive the discretization of the continuous data. (2) For a large database, correlated attribute groups must first be obtained by attribute clustering before (1) can be applied. Based on (1) and (2), pattern discovery methods are developed for mixed-mode data. Extensive experiments using synthetic and real world data were conducted to validate the usefulness and effectiveness of the proposed method.

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Wong, A. K. C., and Li, G. C. L. 2010. Association pattern analysis for pattern pruning, pattern clustering and summarization, to appear in Journal of Knowledge and Information Systems, 2010.

Cited By

Zhou PSze-To AWong A(2018)Discovery and disentanglement of aligned residue associations from aligned pattern clusters to reveal subgroup characteristicsBMC Medical Genomics10.1186/s12920-018-0417-z11:S5Online publication date: 20-Nov-2018
https://doi.org/10.1186/s12920-018-0417-z
Wu GChan K(2018)Clustering driving trip trajectory data based on pattern discovery techniques2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367726(453-457)Online publication date: Mar-2018
https://doi.org/10.1109/ICBDA.2018.8367726
Wu GChan K(2018)Mining spatio-temporal patterns in multivariate spatial time series2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367650(50-54)Online publication date: Mar-2018
https://doi.org/10.1109/ICBDA.2018.8367650
Show More Cited By

Index Terms

Pattern discovery for large mixed-mode database
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

Copyright © 2010 ACM.

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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Publication History

Published: 26 October 2010

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October 26 - 30, 2010

ON, Toronto, Canada

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Cited By

Zhou PSze-To AWong A(2018)Discovery and disentanglement of aligned residue associations from aligned pattern clusters to reveal subgroup characteristicsBMC Medical Genomics10.1186/s12920-018-0417-z11:S5Online publication date: 20-Nov-2018
https://doi.org/10.1186/s12920-018-0417-z
Wu GChan K(2018)Clustering driving trip trajectory data based on pattern discovery techniques2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367726(453-457)Online publication date: Mar-2018
https://doi.org/10.1109/ICBDA.2018.8367726
Wu GChan K(2018)Mining spatio-temporal patterns in multivariate spatial time series2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367650(50-54)Online publication date: Mar-2018
https://doi.org/10.1109/ICBDA.2018.8367650
Wu GChan K(2017)Privacy-preserving trajectory classification of driving trip data based on pattern discovery techniques2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258383(3816-3825)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258383
Zhou PLi GWong A(2016)An Effective Pattern Pruning and Summarization Method Retaining High Quality Patterns With High Area Coverage in Relational DatasetsIEEE Access10.1109/ACCESS.2016.26244184(7847-7858)Online publication date: 2016
https://doi.org/10.1109/ACCESS.2016.2624418
Zhou PChan K(2014)A Model-Based Multivariate Time Series Clustering AlgorithmTrends and Applications in Knowledge Discovery and Data Mining10.1007/978-3-319-13186-3_72(805-817)Online publication date: 26-Nov-2014
https://doi.org/10.1007/978-3-319-13186-3_72
Gene PKeith CAndrew KWu B(2010)Unsupervised discovery of fuzzy patterns in gene expression data2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2010.5706575(269-273)Online publication date: Dec-2010
https://doi.org/10.1109/BIBM.2010.5706575

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