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» Assessing Data Mining Results on Matrices with Randomization
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KDD
2004
ACM
190views Data Mining» more  KDD 2004»
14 years 9 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
SDM
2009
SIAM
118views Data Mining» more  SDM 2009»
14 years 5 months ago
Finding Links and Initiators: A Graph-Reconstruction Problem.
Consider a 0–1 observation matrix M, where rows correspond to entities and columns correspond to signals; a value of 1 (or 0) in cell (i, j) of M indicates that signal j has bee...
Evimaria Terzi, Heikki Mannila
ICDM
2010
IEEE
164views Data Mining» more  ICDM 2010»
13 years 6 months ago
On Finding Similar Items in a Stream of Transactions
While there has been a lot of work on finding frequent itemsets in transaction data streams, none of these solve the problem of finding similar pairs according to standard similar...
Andrea Campagna, Rasmus Pagh
ICDM
2003
IEEE
125views Data Mining» more  ICDM 2003»
14 years 1 months ago
Clustering Item Data Sets with Association-Taxonomy Similarity
We explore in this paper the efficient clustering of item data. Different from those of the traditional data, the features of item data are known to be of high dimensionality and...
Ching-Huang Yun, Kun-Ta Chuang, Ming-Syan Chen
IRI
2003
IEEE
14 years 1 months ago
Damage Pattern Mining in Hurricane Image Databases
– We present a damage pattern mining framework for hurricane data on residential houses using aerial photographs with 1:3000 based scale. The vertical photographs are normally co...
Shu-Ching Chen, Mei-Ling Shyu, Chengcui Zhang, Wal...