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» Clustering Improves the Exploration of Graph Mining Results
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PAKDD
2010
ACM
193views Data Mining» more  PAKDD 2010»
14 years 10 months ago
As Time Goes by: Discovering Eras in Evolving Social Networks
Abstract. Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolutio...
Michele Berlingerio, Michele Coscia, Fosca Giannot...
ICDM
2002
IEEE
191views Data Mining» more  ICDM 2002»
15 years 4 months ago
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...
Inderjit S. Dhillon, Yuqiang Guan, J. Kogan
KDD
2007
ACM
184views Data Mining» more  KDD 2007»
16 years 5 days ago
Correlation search in graph databases
Correlation mining has gained great success in many application domains for its ability to capture the underlying dependency between objects. However, the research of correlation ...
Yiping Ke, James Cheng, Wilfred Ng
WSDM
2012
ACM
304views Data Mining» more  WSDM 2012»
13 years 7 months ago
Beyond co-occurrence: discovering and visualizing tag relationships from geo-spatial and temporal similarities
Studying relationships between keyword tags on social sharing websites has become a popular topic of research, both to improve tag suggestion systems and to discover connections b...
Haipeng Zhang, Mohammed Korayem, Erkang You, David...
INFOCOM
2011
IEEE
14 years 3 months ago
Network-aware behavior clustering of Internet end hosts
Abstract—This paper explores the behavior similarity of Internet end hosts in the same network prefixes. We use bipartite graphs to model network traffic, and then construct on...
Kuai Xu, Feng Wang 0002, Lin Gu