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KDD
2010
ACM
245views Data Mining» more  KDD 2010»
14 years 1 months ago
Flexible constrained spectral clustering
Constrained clustering has been well-studied for algorithms like K-means and hierarchical agglomerative clustering. However, how to encode constraints into spectral clustering rem...
Xiang Wang, Ian Davidson
CIKM
2008
Springer
13 years 11 months ago
A consensus based approach to constrained clustering of software requirements
Managing large-scale software projects involves a number of activities such as viewpoint extraction, feature detection, and requirements management, all of which require a human a...
Chuan Duan, Jane Cleland-Huang, Bamshad Mobasher
PVLDB
2008
107views more  PVLDB 2008»
13 years 8 months ago
Constrained locally weighted clustering
Data clustering is a difficult problem due to the complex and heterogeneous natures of multidimensional data. To improve clustering accuracy, we propose a scheme to capture the lo...
Hao Cheng, Kien A. Hua, Khanh Vu
CORR
2008
Springer
107views Education» more  CORR 2008»
13 years 10 months ago
Maximum Entropy Rate of Markov Sources for Systems With Non-regular Constraints
Using the concept of discrete noiseless channels, it was shown by Shannon in A Mathematical Theory of Communication that the ultimate performance of an encoder for a constrained sy...
Georg Böcherer, Valdemar Cardoso da Rocha Jr....
AAAI
2006
13 years 11 months ago
When Is Constrained Clustering Beneficial, and Why?
Several researchers have illustrated that constraints can improve the results of a variety of clustering algorithms. However, there can be a large variation in this improvement, e...
Kiri Wagstaff, Sugato Basu, Ian Davidson