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KDD
2005
ACM
160views Data Mining» more  KDD 2005»
16 years 1 months ago
Optimizing time series discretization for knowledge discovery
Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the temporal ...
Alfred Ultsch, Fabian Mörchen
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
16 years 1 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
KDD
2004
ACM
170views Data Mining» more  KDD 2004»
16 years 1 months ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
KDD
2003
ACM
124views Data Mining» more  KDD 2003»
16 years 1 months ago
Information-theoretic co-clustering
Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingenc...
Inderjit S. Dhillon, Subramanyam Mallela, Dharmend...
KDD
2002
ACM
149views Data Mining» more  KDD 2002»
16 years 1 months ago
A system for real-time competitive market intelligence
A method is described for real-time market intelligence and competitive analysis. News stories are collected online for a designated group of companies. The goal is to detect crit...
Sholom M. Weiss, Naval K. Verma