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ICPR
2008
IEEE
16 years 19 days ago
Unsupervised clustering using hyperclique pattern constraints
A novel unsupervised clustering algorithm called Hyperclique Pattern-KMEANS (HP-KMEANS) is presented. Considering recent success in semisupervised clustering using pair-wise const...
Yuchou Chang, Dah-Jye Lee, James K. Archibald, Yi ...
IJUFKS
2007
108views more  IJUFKS 2007»
15 years 6 months ago
Resampling for Fuzzy Clustering
Abstract. Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the nu...
Christian Borgelt
CIDM
2007
IEEE
16 years 16 days ago
Scalable Clustering for Large High-Dimensional Data Based on Data Summarization
Clustering large data sets with high dimensionality is a challenging data-mining task. This paper presents a framework to perform such a task efficiently. It is based on the notio...
Ying Lai, Ratko Orlandic, Wai Gen Yee, Sachin Kulk...
AUSDM
2006
Springer
136views Data Mining» more  AUSDM 2006»
15 years 10 months ago
Data Mining in Conceptualising Active Ageing
The concept of older adults contributing to society in a meaningful way has been termed `active ageing'. We present applications of data mining techniques on the active agein...
Richi Nayak, Laurie Buys, Jan Lovie-Kitchin
FUZZIEEE
2007
IEEE
16 years 16 days ago
Prototype-less Fuzzy Clustering
Abstract—In contrast to standard fuzzy clustering, which optimizes a set of prototypes, one for each cluster, this paper studies fuzzy clustering without prototypes. Starting fro...
Christian Borgelt