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ICA
2004
Springer
15 years 3 months ago
Postnonlinear Overcomplete Blind Source Separation Using Sparse Sources
Abstract. We present an approach for blindly decomposing an observed random vector x into f(As) where f is a diagonal function i.e. f = f1 × . . . × fm with one-dimensional funct...
Fabian J. Theis, Shun-ichi Amari
FIMI
2003
146views Data Mining» more  FIMI 2003»
14 years 11 months ago
Mining Frequent Itemsets using Patricia Tries
We present a depth-first algorithm, PatriciaMine, that discovers all frequent itemsets in a dataset, for a given support threshold. The algorithm is main-memory based and employs...
Andrea Pietracaprina, Dario Zandolin
84
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KR
2004
Springer
15 years 3 months ago
Knowledge Discovery by Reversing Inductive Knowledge Representation
In a very basic sense, the aim of knowledge discovery is to reveal structures of knowledge which can be seen as being represented by structural relationships. In this paper, we ma...
Gabriele Kern-Isberner, Jens Fisseler
STACS
2007
Springer
15 years 4 months ago
Small Space Representations for Metric Min-Sum k -Clustering and Their Applications
The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C1, . . . , Ck ⊆ P such that k i=1 p,q∈Ci d(p, q) is minimized. We show the first effi...
Artur Czumaj, Christian Sohler
KDD
2006
ACM
213views Data Mining» more  KDD 2006»
15 years 10 months ago
Learning sparse metrics via linear programming
Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficie...
Glenn Fung, Rómer Rosales