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SDM
2009
SIAM
192views Data Mining» more  SDM 2009»
16 years 1 months ago
Mining Cohesive Patterns from Graphs with Feature Vectors.
The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to g...
Arash Rafiey, Flavia Moser, Martin Ester, Recep Co...
ICDM
2007
IEEE
149views Data Mining» more  ICDM 2007»
15 years 10 months ago
Non-redundant Multi-view Clustering via Orthogonalization
Typical clustering algorithms output a single clustering of the data. However, in real world applications, data can often be interpreted in many different ways; data can have diff...
Ying Cui, Xiaoli Z. Fern, Jennifer G. Dy
MM
2005
ACM
122views Multimedia» more  MM 2005»
15 years 9 months ago
Image clustering with tensor representation
We consider the problem of image representation and clustering. Traditionally, an n1 × n2 image is represented by a vector in the Euclidean space Rn1×n2 . Some learning algorith...
Xiaofei He, Deng Cai, Haifeng Liu, Jiawei Han
GECCO
2005
Springer
102views Optimization» more  GECCO 2005»
15 years 9 months ago
Latent variable crossover for k-tablet structures and its application to lens design problems
This paper presents the Real-coded Genetic Algorithms for high-dimensional ill-scaled structures, what is called, the ktablet structure. The k-tablet structure is the landscape th...
Jun Sakuma, Shigenobu Kobayashi
KES
1999
Springer
15 years 8 months ago
Dynamic Demes parallel genetic algorithm
Abstract—Dynamic Demes is a new method for the parallelisation of evolutionary algorithms. It was derived as a combination of two other parallelisation algorithms: the master-sla...
Mariusz Nowostawski, Riccardo Poli