Article

Discovering interesting patterns through user's interactive feedback

Authors:

Dong Xin,

Xuehua Shen,

Qiaozhu Mei,

Jiawei HanAuthors Info & Claims

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

Pages 773 - 778

https://doi.org/10.1145/1150402.1150502

Published: 20 August 2006 Publication History

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Abstract

In this paper, we study the problem of discovering interesting patterns through user's interactive feedback. We assume a set of candidate patterns (ie, frequent patterns) has already been mined. Our goal is to help a particular user effectively discover interesting patterns according to his specific interest. Without requiring a user to explicitly construct a prior knowledge to measure the interestingness of patterns, we learn the user's prior knowledge from his interactive feedback. We propose two models to represent a user's prior: the log linear model and biased belief model. The former is designed for item-set patterns, whereas the latter is also applicable to sequential and structural patterns. To learn these models, we present a two-stage approach, progressive shrinking and clustering, to select sample patterns for feedback. The experimental results on real and synthetic data sets demonstrate the effectiveness of our approach.

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Cited By

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Lehembre ECremilleux BZimmermann ACuissart BOuali A(2024)WaveLSea: helping experts interactively explore pattern mining search spacesData Mining and Knowledge Discovery10.1007/s10618-024-01037-838:4(2403-2439)Online publication date: 26-May-2024
https://doi.org/10.1007/s10618-024-01037-8
Belmecheri NAribi NLazaar NLebbah YLoudni S(2023)Boosting the Learning for Ranking PatternsAlgorithms10.3390/a1605021816:5(218)Online publication date: 24-Apr-2023
https://doi.org/10.3390/a16050218
Bao XGu TChang LXu ZLi L(2022)Knowledge-Based Interactive Postmining of User-Preferred Co-Location Patterns Using OntologiesIEEE Transactions on Cybernetics10.1109/TCYB.2021.305492352:9(9467-9480)Online publication date: Sep-2022
https://doi.org/10.1109/TCYB.2021.3054923
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Index Terms

Discovering interesting patterns through user's interactive feedback
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

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

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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Association for Computing Machinery

New York, NY, United States

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Published: 20 August 2006

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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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Lehembre ECremilleux BZimmermann ACuissart BOuali A(2024)WaveLSea: helping experts interactively explore pattern mining search spacesData Mining and Knowledge Discovery10.1007/s10618-024-01037-838:4(2403-2439)Online publication date: 26-May-2024
https://doi.org/10.1007/s10618-024-01037-8
Belmecheri NAribi NLazaar NLebbah YLoudni S(2023)Boosting the Learning for Ranking PatternsAlgorithms10.3390/a1605021816:5(218)Online publication date: 24-Apr-2023
https://doi.org/10.3390/a16050218
Bao XGu TChang LXu ZLi L(2022)Knowledge-Based Interactive Postmining of User-Preferred Co-Location Patterns Using OntologiesIEEE Transactions on Cybernetics10.1109/TCYB.2021.305492352:9(9467-9480)Online publication date: Sep-2022
https://doi.org/10.1109/TCYB.2021.3054923
Yu YWang WWu NLiu HShao M(2022)IISDKnowledge-Based Systems10.1016/j.knosys.2021.108080240:COnline publication date: 15-Mar-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.108080
Bouarour NBenouaret IAmer-Yahia S(2022)Optimizing Data Coverage and Significance in Multiple Hypothesis Testing on User GroupsTransactions on Large-Scale Data- and Knowledge-Centered Systems LI10.1007/978-3-662-66111-6_3(64-96)Online publication date: 8-Oct-2022
https://doi.org/10.1007/978-3-662-66111-6_3
Chang LZhang YBao XGu T(2022)IDMBS: An Interactive System to Find Interesting Co-location Patterns Using SVMDatabase Systems for Advanced Applications10.1007/978-3-031-00129-1_47(518-521)Online publication date: 11-Apr-2022
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Omidvar-Tehrani BAmer-Yahia S(2020)User Group Analytics Survey and Research OpportunitiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291365132:10(2040-2059)Online publication date: 1-Oct-2020
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Omidvar-Tehrani BAmer-Yahia SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Data Pipelines for User Group AnalyticsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3314028(2048-2053)Online publication date: 25-Jun-2019
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Wang LBao XZhou L(2018)Redundancy Reduction for Prevalent Co-Location PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.275911030:1(142-155)Online publication date: 1-Jan-2018
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Discovering Interesting Patterns from Hypergraphs

Discovering interesting patterns and associations in data streams

Discovering Significant Patterns

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