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ICDM
2008
IEEE
142views Data Mining» more  ICDM 2008»
14 years 4 months ago
Clustering Events on Streams Using Complex Context Information
Monitoring applications play an increasingly important role in many domains. They detect events in monitored systems and take actions such as invoke a program or notify an adminis...
YongChul Kwon, Wing Yee Lee, Magdalena Balazinska,...
ICDM
2007
IEEE
149views Data Mining» more  ICDM 2007»
14 years 4 months ago
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization
Consensus clustering and semi-supervised clustering are important extensions of the standard clustering paradigm. Consensus clustering (also known as aggregation of clustering) ca...
Tao Li, Chris H. Q. Ding, Michael I. Jordan
PAKDD
2004
ACM
113views Data Mining» more  PAKDD 2004»
14 years 3 months ago
Logistic Regression and Boosting for Labeled Bags of Instances
Abstract. In this paper we upgrade linear logistic regression and boosting to multi-instance data, where each example consists of a labeled bag of instances. This is done by connec...
Xin Xu, Eibe Frank
PAKDD
2009
ACM
151views Data Mining» more  PAKDD 2009»
14 years 4 months ago
Budget Semi-supervised Learning
In this paper we propose to study budget semi-supervised learning, i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/...
Zhi-Hua Zhou, Michael Ng, Qiao-Qiao She, Yuan Jian...
PAKDD
2007
ACM
224views Data Mining» more  PAKDD 2007»
14 years 3 months ago
Graph Nodes Clustering Based on the Commute-Time Kernel
This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel...
Luh Yen, François Fouss, Christine Decaeste...