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» Clustering Improves the Exploration of Graph Mining Results
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DSS
2006
76views more  DSS 2006»
13 years 7 months ago
Mining web navigations for intelligence
The Internet is one of the fastest growing areas of intelligence gathering. We present a statistical approach, called principal clusters analysis, for analyzing millions of user n...
Harris Wu, Michael D. Gordon, Kurt DeMaagd, Weiguo...
DKE
2007
115views more  DKE 2007»
13 years 7 months ago
An improved methodology on information distillation by mining program source code
This paper presents a methodology for knowledge acquisition from source code. We use data mining to support semiautomated software maintenance and comprehension and provide practi...
Yiannis Kanellopoulos, Christos Makris, Christos T...
INFOSCALE
2007
ACM
13 years 9 months ago
Exploring lattice structures in mining multi-domain sequential patterns
— Since sequential patterns may exist in multiple sequence databases, we propose algorithm PropagatedMine+ to efficiently discover multi-domain sequential patterns. Prior works ...
Zhung-Xun Liao, Wen-Chih Peng
GFKL
2005
Springer
95views Data Mining» more  GFKL 2005»
14 years 28 days ago
An Indicator for the Number of Clusters: Using a Linear Map to Simplex Structure
Abstract. The problem of clustering data can be formulated as a graph partitioning problem. In this setting, spectral methods for obtaining optimal solutions have received a lot of...
Marcus Weber, Wasinee Rungsarityotin, Alexander Sc...
ICPP
2000
IEEE
13 years 11 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary