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» Large Scale Data Mining: Challenges and Responses
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KDD
2010
ACM
300views Data Mining» more  KDD 2010»
15 years 10 months ago
Mining top-k frequent items in a data stream with flexible sliding windows
We study the problem of finding the k most frequent items in a stream of items for the recently proposed max-frequency measure. Based on the properties of an item, the maxfrequen...
Hoang Thanh Lam, Toon Calders
SDM
2009
SIAM
184views Data Mining» more  SDM 2009»
16 years 3 months ago
DensEst: Density Estimation for Data Mining in High Dimensional Spaces.
Subspace clustering and frequent itemset mining via “stepby-step” algorithms that search the subspace/pattern lattice in a top-down or bottom-up fashion do not scale to large ...
Emmanuel Müller, Ira Assent, Ralph Krieger, S...
DMIN
2006
115views Data Mining» more  DMIN 2006»
15 years 7 months ago
Data Mining Techniques to Study Therapy Success with Autistic Children
Autism spectrum disorder has become one of the most prevalent developmental disorders, characterized by a wide variety of symptoms. Many children need extensive therapy for years t...
Gondy Leroy, Annika Irmscher, Marjorie H. Charlop-...
WSDM
2010
ACM
160views Data Mining» more  WSDM 2010»
16 years 3 months ago
Tagging Human Knowledge
A fundamental premise of tagging systems is that regular users can organize large collections for browsing and other tasks using uncontrolled vocabularies. Until now, that premise...
Paul Heymann, Andreas Paepcke, Hector Garcia-Molin...
ICDM
2005
IEEE
188views Data Mining» more  ICDM 2005»
15 years 11 months ago
CLUMP: A Scalable and Robust Framework for Structure Discovery
We introduce a robust and efficient framework called CLUMP (CLustering Using Multiple Prototypes) for unsupervised discovery of structure in data. CLUMP relies on finding multip...
Kunal Punera, Joydeep Ghosh