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KDD
2012
ACM
221views Data Mining» more  KDD 2012»
13 years 9 months ago
Fast mining and forecasting of complex time-stamped events
Given huge collections of time-evolving events such as web-click logs, which consist of multiple attributes (e.g., URL, userID, timestamp), how do we find patterns and trends? Ho...
Yasuko Matsubara, Yasushi Sakurai, Christos Falout...
KDD
2012
ACM
242views Data Mining» more  KDD 2012»
13 years 9 months ago
Query-driven discovery of semantically similar substructures in heterogeneous networks
Heterogeneous information networks that contain multiple types of objects and links are ubiquitous in the real world, such as bibliographic networks, cyber-physical networks, and ...
Xiao Yu, Yizhou Sun, Peixiang Zhao, Jiawei Han
KDD
2012
ACM
178views Data Mining» more  KDD 2012»
13 years 9 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
SIGIR
2012
ACM
13 years 9 months ago
Automatic suggestion of query-rewrite rules for enterprise search
Enterprise search is challenging for several reasons, notably the dynamic terminology and jargon that are specific to the enterprise domain. This challenge is partly addressed by...
Zhuowei Bao, Benny Kimelfeld, Yunyao Li
GIS
2008
ACM
16 years 7 months ago
Density based co-location pattern discovery
Co-location pattern discovery is to find classes of spatial objects that are frequently located together. For example, if two categories of businesses often locate together, they ...
Xiangye Xiao, Xing Xie, Qiong Luo, Wei-Ying Ma
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