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SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
13 years 8 months ago
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Levent Ertöz, Michael Steinbach, Vipin Kumar
AUSAI
2009
Springer
13 years 10 months ago
Adapting Spectral Co-clustering to Documents and Terms Using Latent Semantic Analysis
Abstract. Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of ...
Laurence A. F. Park, Christopher Leckie, Kotagiri ...
WEBI
2005
Springer
14 years 29 days ago
Integrating Element and Term Semantics for Similarity-Based XML Document Clustering
Structured link vector model (SLVM) is a recently proposed document representation that takes into account both structural and semantic information for measuring XML document simi...
Jianwu Yang, William K. Cheung, Xiaoou Chen
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
14 years 8 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
RECOMB
2005
Springer
14 years 7 months ago
The Incompatible Desiderata of Gene Cluster Properties
Abstract. There is widespread interest in comparative genomics in determining if historically and/or functionally related genes are spatially clustered in the genome, and whether t...
Rose Hoberman, Dannie Durand