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» Mining Sequential Patterns Using Graph Search Techniques
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KDD
2006
ACM
198views Data Mining» more  KDD 2006»
15 years 9 months ago
Event detection from evolution of click-through data
Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose t...
Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, Wei...
WSDM
2009
ACM
114views Data Mining» more  WSDM 2009»
15 years 4 months ago
Wikipedia pages as entry points for book search
A lot of the world’s knowledge is stored in books, which, as a result of recent mass-digitisation efforts, are increasingly available online. Search engines, such as Google Book...
Marijn Koolen, Gabriella Kazai, Nick Craswell
89
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SDM
2007
SIAM
143views Data Mining» more  SDM 2007»
14 years 11 months ago
Less is More: Compact Matrix Decomposition for Large Sparse Graphs
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
Jimeng Sun, Yinglian Xie, Hui Zhang, Christos Falo...
88
Voted
PETRA
2009
ACM
15 years 4 months ago
Towards faster activity search using embedding-based subsequence matching
Event search is the problem of identifying events or activity of interest in a large database storing long sequences of activity. In this paper, our topic is the problem of identi...
Panagiotis Papapetrou, Paul Doliotis, Vassilis Ath...
CIKM
2009
Springer
15 years 2 months ago
iMecho: an associative memory based desktop search system
Traditional desktop search engines only support keyword based search that needs exact keyword matching to find resources. However, users generally have a vague picture of what is...
Jidong Chen, Hang Guo, Wentao Wu, Wei Wang 0009