Effcient Mining of Heterogeneous Star-Structured Data
Received:October 10, 2008  Revised:December 20, 2008  Download PDF
Manjeet Rege,Qi Yu. Effcient Mining of Heterogeneous Star-Structured Data. International Journal of Software and Informatics, 2008,2(2):141~161
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Manjeet Rege  Qi Yu
Abstract:Many of the real world clustering problems arising in data mining applications are heterogeneous in nature. Heterogeneous co-clustering involves simultaneous clustering of objects of two or more data types. While pairwise co-clustering of two data types has been well studied in the literature, research on high-order heterogeneous co-clustering is still limited. In this paper, we propose a graph theoretical framework for addressing starstructured co-clustering problems in which a central data type is connected to all the other data types. Partitioning this graph leads to co-clustering of all the data types under the constraints of the star-structure. Although, graph partitioning approach has been adopted before to address star-structured heterogeneous complex problems, the main contribution of this work lies in an e cient algorithm that we propose for partitioning the star-structured graph. Computationally, our algorithm is very quick as it requires a simple solution to a sparse system of overdetermined linear equations. Theoretical analysis and extensive experiments performed on toy and real datasets demonstrate the quality, e ciency and stability of the proposed algorithm.
keywords:Co-clustering  High-Order Heterogeneous Data  Isoperimetric  Consistency
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