An important and yet unsolved problem in unsupervised data clustering is how to determine the number of clusters. The … technique uses the output of any clustering algorithm and identifies the maximum number of groups that breaks down the structure … examples) some popular methods that have been proposed in the literature. Applications in graph clustering, in iris and breast …

parks, based on the cascade application of a Self-Organizing Map (SOM) and the clustering k-means algorithm. The system is …

Minimum sum-of-squares clustering consists in partitioning a given set of n points into c clusters in order to minimize …

Abraham et al (2003), which combines spline interpolation with k-means clustering. The findings indicate the presence of two …

clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for … selecting the number of clusters through cross validation. In this paper, an estimation scheme for clustering instability is … developed based on the bootstrap, and then the number of clusters is selected so that the corresponding estimated clustering …

Information Integration Theory (IIT) is a theory of judgment in daily life. Its principal aim is to study the cognitive … pattern involves many factors. This paper builds on the cluster analysis tradition for developing a series of clustering …