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» Combining Two Data Mining Methods for System Identification
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CIDM
2009
IEEE
14 years 1 months ago
An architecture and algorithms for multi-run clustering
—This paper addresses two main challenges for clustering which require extensive human effort: selecting appropriate parameters for an arbitrary clustering algorithm and identify...
Rachsuda Jiamthapthaksin, Christoph F. Eick, Vadee...
WWW
2007
ACM
14 years 9 months ago
Providing session management as core business service
It is extremely hard for a global organization with services over multiple channels to capture a consistent and unified view of its data, services, and interactions. While SOA and...
Ismail Ari, Jun Li, Riddhiman Ghosh, Mohamed Dekhi...
ISCI
2007
84views more  ISCI 2007»
13 years 8 months ago
Simulating continuous fuzzy systems
: In our book to appear in print from Springer-Verlag GmbH, Simulating Continuous Fuzzy Systems, Buckley and Jowers, we use crisp continuous simulation under Matlab™/Simulink™ ...
Leonard J. Jowers, James J. Buckley, Kevin D. Reil...
ICSM
2005
IEEE
14 years 2 months ago
Co-Change Visualization
Clustering layouts of software systems combine two important aspects: they reveal groups of related artifacts of the software system, and they produce a visualization of the resul...
Dirk Beyer
ICDM
2003
IEEE
92views Data Mining» more  ICDM 2003»
14 years 2 months ago
Validating and Refining Clusters via Visual Rendering
Clustering is an important technique for understanding and analysis of large multi-dimensional datasets in many scientific applications. Most of clustering research to date has be...
Keke Chen, Ling Liu