Spanning Tree Based Attribute Clustering

Authors:
Yifeng Zeng;Jorge Cordero Hernandez;Shuyuan Lin
Affiliations:
Department of Computer Science, Aalborg University, Aalborg, Denmark DK-9220 and Department of Computer Science, Fuzhou University, FuJian, P.R.China;Department of Computer Science, Aalborg University, Aalborg, Denmark DK-9220 and Department of Computer Science, Fuzhou University, FuJian, P.R.China;Department of Computer Science, Aalborg University, Aalborg, Denmark DK-9220 and Department of Computer Science, Fuzhou University, FuJian, P.R.China
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

Citing 4
Cited 0

Clustering algorithms based on minimum and maximum spanning trees

SCG '88 Proceedings of the fourth annual symposium on Computational geometry
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

Attribute clustering has been previously employed to detect statistical dependence between subsets of variables. We propose a novel attribute clustering algorithm motivated by research of complex networks, called the Star Discovery algorithm. The algorithm partitions and indirectly discards inconsistent edges from a maximum spanning tree by starting appropriate initial modes, therefore generating stable clusters. It discovers sound clusters through simple graph operations and achieves significant computational savings. We compare the Star Discovery algorithm against earlier attribute clustering algorithms and evaluate the performance in several domains.