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KDD
2010
ACM
245views Data Mining» more  KDD 2010»
15 years 4 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
15 years 3 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»
14 years 11 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»
15 years 1 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
15 years 2 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