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EUROPAR
1999
Springer
14 years 21 days ago
Parallel k/h-Means Clustering for Large Data Sets
This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We ...
Kilian Stoffel, Abdelkader Belkoniene
SIGMOD
1998
ACM
271views Database» more  SIGMOD 1998»
13 years 8 months ago
Towards On-Line Analytical Mining in Large Databases
Great e orts have been paid in the Intelligent Database Systems Research Lab for the research and development of e cient data mining methods and construction of on-line analytical...
Jiawei Han
ICAI
2010
13 years 6 months ago
Using Sensor Sequences for Activity Recognition by Mining and Multi-Class Adaboost
- In this paper, we present an activity recognition system using sensor sequence information generated from many binary on-off state sensors. When many sensors are deployed the num...
Md. Kamrul Hasan, Sungyoung Lee, Young-Koo Lee
WSDM
2012
ACM
252views Data Mining» more  WSDM 2012»
12 years 4 months ago
WebSets: extracting sets of entities from the web using unsupervised information extraction
We describe a open-domain information extraction method for extracting concept-instance pairs from an HTML corpus. Most earlier approaches to this problem rely on combining cluste...
Bhavana Bharat Dalvi, William W. Cohen, Jamie Call...
IPMI
2007
Springer
14 years 9 months ago
Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework
Abstract. We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries,...
Kilian M. Pohl, Ron Kikinis, William M. Wells III