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CIKM
2006
Springer
14 years 11 months ago
Efficiently clustering transactional data with weighted coverage density
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
Hua Yan, Keke Chen, Ling Liu
BMCBI
2004
158views more  BMCBI 2004»
14 years 9 months ago
Incremental genetic K-means algorithm and its application in gene expression data analysis
Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms suc...
Yi Lu, Shiyong Lu, Farshad Fotouhi, Youping Deng, ...
GIS
2002
ACM
14 years 9 months ago
Opening the black box: interactive hierarchical clustering for multivariate spatial patterns
Clustering is one of the most important tasks for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial c...
Diansheng Guo, Donna Peuquet, Mark Gahegan
EDM
2010
129views Data Mining» more  EDM 2010»
14 years 11 months ago
Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers
While students' skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [...
Rebecca Nugent, Nema Dean, Elizabeth Ayers
CSB
2004
IEEE
136views Bioinformatics» more  CSB 2004»
15 years 1 months ago
Minimum Entropy Clustering and Applications to Gene Expression Analysis
Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First,...
Haifeng Li, Keshu Zhang, Tao Jiang