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Title:
a general framework to encode heterogeneous information sources for contextual pattern mining
Author: Dong Weishan ; Fan Wei ; Shi Leib ; Zhou Changjin ; Yan Xifeng
Source: ACM International Conference Proceeding Series
Conference Name: 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Conference Date: October 29, 2012 - November 2, 2012
Issued Date: 2012
Conference Place: Maui, HI, United states
Keyword: Algorithms ; Data mining ; Knowledge management
Indexed Type: EI
ISBN: 9781450311564
Department: (1) IBM Research China; (2) Huawei Noah's Ark Lab. China; (3) Institute of Software Chinese Academy of Sciences China; (4) University of California Santa Barbara CA United States
Sponsorship: Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB
Abstract: Traditional pattern mining methods usually work on single data sources. However, in practice, there are often multiple and heterogeneous information sources. They collectively provide contextual information not available in any single source alone describing the same set of objects, and are useful for discovering hidden contextual patterns. One important challenge is to provide a general methodology to mine contextual patterns easily and efficiently. In this paper, we propose a general framework to encode contextual information from multiple sources into a coherent representation - -Contextual Information Graph (CIG). The complexity of the encoding scheme is linear in both time and space. More importantly, CIG can be handled by any single-source pattern mining algorithms that accept taxonomies without any modification. We demonstrate by three applications of the contextual association rule, sequence and graph mining, that contextual patterns providing rich and insightful knowledge can be easily discovered by the proposed framework. It enables Contextual Pattern Mining (CPM) by reusing single-source methods, and is easy to deploy and use in real-world systems. © 2012 ACM.
English Abstract: Traditional pattern mining methods usually work on single data sources. However, in practice, there are often multiple and heterogeneous information sources. They collectively provide contextual information not available in any single source alone describing the same set of objects, and are useful for discovering hidden contextual patterns. One important challenge is to provide a general methodology to mine contextual patterns easily and efficiently. In this paper, we propose a general framework to encode contextual information from multiple sources into a coherent representation - -Contextual Information Graph (CIG). The complexity of the encoding scheme is linear in both time and space. More importantly, CIG can be handled by any single-source pattern mining algorithms that accept taxonomies without any modification. We demonstrate by three applications of the contextual association rule, sequence and graph mining, that contextual patterns providing rich and insightful knowledge can be easily discovered by the proposed framework. It enables Contextual Pattern Mining (CPM) by reusing single-source methods, and is easy to deploy and use in real-world systems. © 2012 ACM.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15824
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Dong Weishan,Fan Wei,Shi Leib,et al. a general framework to encode heterogeneous information sources for contextual pattern mining[C]. 见:21st ACM International Conference on Information and Knowledge Management, CIKM 2012. Maui, HI, United states. October 29, 2012 - November 2, 2012.
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