LinkClus: Efficient clustering via heterogeneous semantic links

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LinkClus: Efficient clustering via heterogeneous semantic links (2006)

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by Xiaoxin Yin , Jiawei Han

Venue:	In VLDB
Citations:	40 - 7 self

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@INPROCEEDINGS{Yin06linkclus:efficient,
    author = {Xiaoxin Yin and Jiawei Han},
    title = {LinkClus: Efficient clustering via heterogeneous semantic links},
    booktitle = {In VLDB},
    year = {2006},
    pages = {427--438}
}

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Abstract

Data objects in a relational database are cross-linked with each other via multi-typed links. Links contain rich semantic information that may indicate important relationships among objects. Most current clustering methods rely only on the properties that belong to the objects per se. However, the similarities between objects are often indicated by the links, and desirable clusters cannot be generated using only the properties of objects. In this paper we explore linkage-based clustering, in which the similarity between two objects is measured based on the similarities between the objects linked with them. In comparison with a previous study (SimRank) that computes links recursively on all pairs of objects, we take advantage of the power law distribution of links, and develop a hierarchical structure called SimTree to represent similarities in multi-granularity manner. This method avoids the high cost of computing and storing pairwise similarities but still thoroughly explore relationships among objects. An efficient algorithm is proposed to compute similarities between objects by avoiding pairwise similarity computations through merging computations that go through the same branches in the SimTree. Experiments show the proposed approach achieves high efficiency, scalability, and accuracy in clustering multi-typed linked objects. 1.

Keyphrases

heterogeneous semantic link previous study desirable cluster current clustering method multi-granularity manner relational database pairwise similarity computation multi-typed link linkage-based clustering high efficiency link contain rich semantic information hierarchical structure data object important relationship efficient algorithm power law distribution pairwise similarity high cost explore relationship

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