Article

Generating semantic annotations for frequent patterns with context analysis

Authors:

ChengXiang ZhaiAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 337 - 346

https://doi.org/10.1145/1150402.1150441

Published: 20 August 2006 Publication History

Abstract

As a fundamental data mining task, frequent pattern mining has widespread applications in many different domains. Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns, but little attention has been paid to the important nextstep - interpreting the discovered frequent patterns. Although some recent work has studied the compression and summarization of frequent patterns, the proposed techniques can only annotate a frequent pattern with non-semantical information (e.g. support), which provides only limited help for a user to understand the patterns.In this paper, we propose the novel problem of generating semantic annotations for frequent patterns. The goal is to annotate a frequent pattern with in-depth, concise, and structured information that can better indicate the hidden meanings of the pattern. We propose a general approach to generate such anannotation for a frequent pattern by constructing its context model, selecting informative context indicators, and extracting representative transactions and semantically similar patterns. This general approach has potentially many applications such as generating a dictionary-like description for a pattern, finding synonym patterns, discovering semantic relations, and summarizing semantic classes of a set of frequent patterns. Experiments on different datasets show that our approach is effective in generating semantic pattern annotations.

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Cited By

Trasierras ALuna JFournier-Viger PVentura S(2024)Data heterogeneity's impact on the performance of frequent itemset mining algorithmsInformation Sciences: an International Journal10.1016/j.ins.2024.120981678:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120981
Zhao QWang CWang PZhou MJiang C(2018)A Novel Method on Information Recommendation via Hybrid SimilarityIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.263357348:3(448-459)Online publication date: Mar-2018
https://doi.org/10.1109/TSMC.2016.2633573
Bashar MLi Y(2018)Interpretation of text patternsData Mining and Knowledge Discovery10.1007/s10618-018-0556-z32:4(849-884)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10618-018-0556-z
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Index Terms

Generating semantic annotations for frequent patterns with context analysis
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Copyright © 2006 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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Published: 20 August 2006

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KDD06

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Trasierras ALuna JFournier-Viger PVentura S(2024)Data heterogeneity's impact on the performance of frequent itemset mining algorithmsInformation Sciences: an International Journal10.1016/j.ins.2024.120981678:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120981
Zhao QWang CWang PZhou MJiang C(2018)A Novel Method on Information Recommendation via Hybrid SimilarityIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2016.263357348:3(448-459)Online publication date: Mar-2018
https://doi.org/10.1109/TSMC.2016.2633573
Bashar MLi Y(2018)Interpretation of text patternsData Mining and Knowledge Discovery10.1007/s10618-018-0556-z32:4(849-884)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10618-018-0556-z
Bashar MLi YShen YGao YHuang W(2017)Conceptual annotation of text patternsComputational Intelligence10.1111/coin.1213333:4(948-979)Online publication date: 26-Jul-2017
https://doi.org/10.1111/coin.12133
Zhao WLiu CWen JLi X(2017)Ranking and tagging bursty features in text streams with context language modelsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5144-z11:5(852-862)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s11704-016-5144-z
Zhai CMassung S(2016)ReferencesText Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining10.1145/2915031.2915054Online publication date: 23-Jun-2016
https://dl.acm.org/doi/10.1145/2915031.2915054
Zhai CMassung S(2016)AppendixesText Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining10.1145/2915031.2915053Online publication date: 23-Jun-2016
https://dl.acm.org/doi/10.1145/2915031.2915053
Zhai CMassung S(2016)Toward A Unified System for Text Management and AnalysisText Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining10.1145/2915031.2915052Online publication date: 23-Jun-2016
https://dl.acm.org/doi/10.1145/2915031.2915052
Zhai CMassung S(2016)Joint Analysis of Text and Structured DataText Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining10.1145/2915031.2915051Online publication date: 23-Jun-2016
https://dl.acm.org/doi/10.1145/2915031.2915051
Zhai CMassung S(2016)Opinion Mining and Sentiment AnalysisText Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining10.1145/2915031.2915050Online publication date: 23-Jun-2016
https://dl.acm.org/doi/10.1145/2915031.2915050
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