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KDD
2008
ACM
119views Data Mining» more  KDD 2008»
16 years 3 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen
137
Voted
KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 3 months ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
153
Voted
KDD
2005
ACM
218views Data Mining» more  KDD 2005»
16 years 3 months ago
A maximum entropy web recommendation system: combining collaborative and content features
Web users display their preferences implicitly by navigating through a sequence of pages or by providing numeric ratings to some items. Web usage mining techniques are used to ext...
Xin Jin, Yanzan Zhou, Bamshad Mobasher
190
Voted
SIGMOD
2008
ACM
144views Database» more  SIGMOD 2008»
16 years 2 months ago
Mining significant graph patterns by leap search
With ever-increasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with user-specified objective functions. ...
Xifeng Yan, Hong Cheng, Jiawei Han, Philip S. Yu
134
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CVPR
2010
IEEE
15 years 11 months ago
Linked Edges as Stable Region Boundaries
Many of the recently popular shape based category recognition methods require stable, connected and labeled edges as input. This paper introduces a novel method to find the most st...
Michael Donoser, Hayko Riemenschneider and Horst B...