2005 | OriginalPaper | Buchkapitel
Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets
verfasst von : Olfa Nasraoui, Elizabeth Leon, Raghu Krishnapuram
Erschienen in: Evolutionary Computation in Data Mining
Verlag: Springer Berlin Heidelberg
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As a valuable unsupervised learning tool, clustering is crucial to many applications in pattern recognition, machine learning, and data mining. Evolutionary techniques have been used with success as global searchers in difficult problems, particularly in the optimization of non-differentiable functions. Hence, they can improve clustering. However, existing
evolutionary
clustering techniques suffer from one or more of the following shortcomings: (i) they are
not robust
in the presence of noise, (ii) they assume a
known
number of clusters, and (iii) the size of the search space
explodes exponentially
with the number of clusters, or with the number of data points. We present a
robust
clustering algorithm, called the
Unsupervised Niche Clustering algorithm (UNC)
, that overcomes all the above difficulties. UNC can successfully find dense areas (clusters) in feature space and determines the
number
of clusters
automatically
. The clustering problem is converted to a multimodal function optimization problem within the context of Genetic Niching. Robust cluster scale estimates are
dynamically
estimated using a hybrid learning scheme coupled with the genetic optimization of the cluster centers, to adapt to clusters of different sizes and noise contamination rates. Genetic optimization enables our approach to handle data with both numeric and qualitative attributes, and general
subjective, non metric, even non-differentiable
dissimilarity measures.