poster

Mining frequent patterns across multiple data streams

Authors:

Jing Guo,

Peng Zhang,

Jianlong Tan,

Li GuoAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 2325 - 2328

https://doi.org/10.1145/2063576.2063957

Published: 24 October 2011 Publication History

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Abstract

Mining frequent patterns from data streams has drawn increasing attention in recent years. However, previous mining algorithms were all focused on a single data stream. In many emerging applications, it is of critical importance to combine multiple data streams for analysis. For example, in real-time news topic analysis, it is necessary to combine multiple news report streams from dierent media sources to discover collaborative frequent patterns which are reported frequently in all media, and comparative frequent patterns which are reported more frequently in a media than others. To address this problem, we propose a novel frequent pattern mining algorithm Hybrid-Streaming, H-Stream for short. H-Stream builds a new Hybrid-Frequent tree to maintain historical frequent and potential frequent itemsets from all data streams, and incrementally updates these itemsets for efficient collaborative and comparative pattern mining. Theoretical and empirical studies demonstrate the utility of the proposed method.

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Cited By

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Lee HShin BChoi DLim JBok KYoo J(2024)Graph Stream Compression Scheme Based on Pattern Dictionary Using ProvenanceApplied Sciences10.3390/app1411455314:11(4553)Online publication date: 25-May-2024
https://doi.org/10.3390/app14114553
Dhakshayani JSivasathya SSharmiladevi SSophie S(2022)Mining Frequent Patterns from Temporal Dataset Using Backtracking Search Tree of GenMax AlgorithmHuman-Centric Smart Computing10.1007/978-981-19-5403-0_9(105-117)Online publication date: 29-Nov-2022
https://doi.org/10.1007/978-981-19-5403-0_9
Wang JFang SLiu CQin JLi XShi Z(2020) Top- closed co-occurrence patterns mining with differential privacy over multiple streams Future Generation Computer Systems10.1016/j.future.2020.04.049111(339-351)Online publication date: Oct-2020
https://doi.org/10.1016/j.future.2020.04.049
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Index Terms

Mining frequent patterns across multiple data streams
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

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

Publication History

Published: 24 October 2011

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CIKM '11

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CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Lee HShin BChoi DLim JBok KYoo J(2024)Graph Stream Compression Scheme Based on Pattern Dictionary Using ProvenanceApplied Sciences10.3390/app1411455314:11(4553)Online publication date: 25-May-2024
https://doi.org/10.3390/app14114553
Dhakshayani JSivasathya SSharmiladevi SSophie S(2022)Mining Frequent Patterns from Temporal Dataset Using Backtracking Search Tree of GenMax AlgorithmHuman-Centric Smart Computing10.1007/978-981-19-5403-0_9(105-117)Online publication date: 29-Nov-2022
https://doi.org/10.1007/978-981-19-5403-0_9
Wang JFang SLiu CQin JLi XShi Z(2020) Top- closed co-occurrence patterns mining with differential privacy over multiple streams Future Generation Computer Systems10.1016/j.future.2020.04.049111(339-351)Online publication date: Oct-2020
https://doi.org/10.1016/j.future.2020.04.049
Goyal PSesh Challa JShrivastava SGoyal N(2020)Anytime Frequent Itemset Mining of Transactional Data StreamsBig Data Research10.1016/j.bdr.2020.100146(100146)Online publication date: Jul-2020
https://doi.org/10.1016/j.bdr.2020.100146
Amagata DHara T(2018)Mining Top-k Co-Occurrence Patterns across Multiple Streams (Extended Abstract)2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00231(1747-1748)Online publication date: Apr-2018
https://doi.org/10.1109/ICDE.2018.00231
Al-Kateb MLee B(2018)Adaptive stratified reservoir sampling over heterogeneous data streamsInformation Systems10.1016/j.is.2012.03.00539(199-216)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1016/j.is.2012.03.005
Liu FWang QWang X(2018)Improved algorithm for parallel mining collaborative frequent itemsets in multiple data streamsCluster Computing10.1007/s10586-018-1859-yOnline publication date: 30-Jan-2018
https://doi.org/10.1007/s10586-018-1859-y
Amagata DHara T(2017)Mining Top-k Co-Occurrence Patterns across Multiple StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.272853729:10(2249-2262)Online publication date: 1-Oct-2017
https://doi.org/10.1109/TKDE.2017.2728537
Shalini Jain S(2017)A frequent itemset reduction algorithm for global pattern mining on distributed data streams2017 Tenth International Conference on Contemporary Computing (IC3)10.1109/IC3.2017.8284320(1-6)Online publication date: Aug-2017
https://doi.org/10.1109/IC3.2017.8284320
Shalini Jain S(2017)A comparative analysis of frequent pattern mining algorithms used for streaming data2017 International Conference on Computing, Communication and Automation (ICCCA)10.1109/CCAA.2017.8229809(250-255)Online publication date: May-2017
https://doi.org/10.1109/CCAA.2017.8229809
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Mining of Frequent Itemsets from Streams of Uncertain Data

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