a general framework to encode heterogeneous information sources for contextual pattern mining
文献类型：会议论文
| 作者 | Dong Weishan ; Fan Wei ; Shi Leib ; Zhou Changjin ; Yan Xifeng |
| 出版日期 | 2012 |
| 会议名称 | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 |
| 会议日期 | October 29, 2012 - November 2, 2012 |
| 会议地点 | Maui, HI, United states |
| 关键词 | Algorithms Data mining Knowledge management |
| 页码 | 65-74 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB |
| 会议录 | ACM International Conference Proceeding Series
 |
| 语种 | 英语 |
| ISBN号 | 9781450311564 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15824]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：软件研究所
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