%0 Journal Article
%T Effcient Mining of Heterogeneous Star-Structured Data<br>
%A Manjeet Rege
%A Qi Yu
%A <br>
%J 软件学报
%D 2008
%I 
%X 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.
%K Co-clustering
%K High-Order Heterogeneous Data
%K Isoperimetric
%K Consistency<br>
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=7DCBBB246D3EE17C52F6812DBE0A9A9F&yid=67289AFF6305E306&vid=0B39A22176CE99FB&iid=0B39A22176CE99FB&sid=A8DE7703CC9E390F&eid=8575BEDA702C4B7C&journal_id=1000-9825&journal_name=软件学报&referenced_num=0&reference_num=31