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136
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EUROPAR
1999
Springer
15 years 7 months 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
165
Voted
SIGMOD
1998
ACM
271views Database» more  SIGMOD 1998»
15 years 2 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
144
Voted
ICAI
2010
15 years 19 days 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
162
Voted
WSDM
2012
ACM
252views Data Mining» more  WSDM 2012»
13 years 10 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...
130
Voted
IPMI
2007
Springer
16 years 3 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