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» Combining Two Data Mining Methods for System Identification
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CIDM
2009
IEEE
15 years 2 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
15 years 10 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...
61
Voted
ISCI
2007
84views more  ISCI 2007»
14 years 9 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
15 years 3 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
88
Voted
ICDM
2003
IEEE
92views Data Mining» more  ICDM 2003»
15 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