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	A similarity reinforcement algorithm for heterogeneous Web pages
	Liu, N ; Yan, J ; Bai, F ; Zhang, BY ; Xi, WS ; Fan, WG ; Chen, Z ; Ji, L ; Hu, CY ; Ma, WY
	2005
英文摘要	Many machine learning and data mining algorithms crucially rely on the similarity metrics. However, most early research works such as Vector Space Model or Latent Semantic Index only used single relationship to measure the similarity of data objects. In this paper, we first use an Intra- and Inter- Type Relationship Matrix (IITRM) to represent a set of heterogeneous data objects and their inter-relationships. Then, we propose a novel similarity-calculating algorithm over the Inter- and Intra- Type Relationship Matrix. It tries to integrate information from heterogeneous sources to serve their purposes by iteratively computing. This algorithm can help detect latent relationships among heterogeneous data objects. Our new algorithm is based on the intuition that the intrarelationship should affect the inter-relationship, and vice versa. Experimental results on the MSN logs dataset show that our algorithm outperforms the traditional Cosine similarity.; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; SCI(E); CPCI-S(ISTP); 0
语种	英语
出处	SCI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/399781]
专题	数学科学学院
推荐引用方式 GB/T 7714	Liu, N,Yan, J,Bai, F,et al. A similarity reinforcement algorithm for heterogeneous Web pages. 2005-01-01.

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