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» Mining Noisy Data Streams via a Discriminative Model
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PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
13 years 10 months ago
Fast and Light Boosting for Adaptive Mining of Data Streams
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Fang Chu, Carlo Zaniolo
KDD
2009
ACM
194views Data Mining» more  KDD 2009»
14 years 5 months ago
Combining link and content for community detection: a discriminative approach
In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most ...
Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
KDD
2010
ACM
326views Data Mining» more  KDD 2010»
13 years 3 months ago
Document clustering via dirichlet process mixture model with feature selection
One essential issue of document clustering is to estimate the appropriate number of clusters for a document collection to which documents should be partitioned. In this paper, we ...
Guan Yu, Ruizhang Huang, Zhaojun Wang
PKDD
2004
Springer
131views Data Mining» more  PKDD 2004»
13 years 10 months ago
Asynchronous and Anticipatory Filter-Stream Based Parallel Algorithm for Frequent Itemset Mining
Abstract In this paper we propose a novel parallel algorithm for frequent itemset mining. The algorithm is based on the filter-stream programming model, in which the frequent item...
Adriano Veloso, Wagner Meira Jr., Renato Ferreira,...
KDD
2012
ACM
178views Data Mining» more  KDD 2012»
11 years 7 months ago
Mining emerging patterns by streaming feature selection
Building an accurate emerging pattern classifier with a highdimensional dataset is a challenging issue. The problem becomes even more difficult if the whole feature space is unava...
Kui Yu, Wei Ding 0003, Dan A. Simovici, Xindong Wu