A method of GA: Genetic Algorithm based ISODATA clustering is proposed.GA clustering is now widely available. One of the problems for GA clustering is a poor clustering performance due to the assumption that clusters are represented as convex functions. Well known ISODATA clustering has parameters of threshold for merge and split. The parameters have to be determined without any assumption (con...

Maximum margin principle has been successfully applied to many supervised and semi-supervised problems in machine learning. Recently, this principle was extended for clustering, referred to as Maximum Margin Clustering (MMC) and achieved promising performance in recent studies. To avoid the problem of local minima, MMC can be solved globally via convex semi-definite programming (SDP) relaxation...

A method of GA: Genetic Algorithm based ISODATA clustering is proposed. GA clustering is now widely available. One of the problems for GA clustering is a poor clustering performance due to the assumption that clusters are represented as convex functions. Well known ISODATA clustering has parameters of threshold for merge and split. The parameters have to be determined without any assumption (co...

Computing not the local, but the global optimum of a cluster assignment is one of the important aspects in clustering. Convex clustering is an approach to acquire the global optimum, assuming some fixed centers and bandwidths of the clusters. The essence of the convex clustering is a convex optimization of the mixture weights whose optimum becomes sparse. One of the limitations in the convex cl...

We have reviewed several state-of-the-art machine learning approaches to different types of link based clustering in this chapter. Specifically, we have presented the spectral clustering for heterogeneous relational data, the symmetric convex coding for homogeneous relational data, the citation model for clustering the special but popular homogeneous relational data – the textual documents with...

Clustering has been among the most active research topics in machine learning and pattern recognition. Though recent approaches have delivered impressive results in a number of challenging clustering tasks, most of them did not solve two problems. First, most approaches need prior knowledge about the number of clusters which is not practical in many applications. Second, non-linear and elongate...

We propose a new technique to perform unsupervised data classification (clustering) based on density induced metric and non-smooth optimization. Our goal is to automatically recognize multidimensional clusters of non-convex shape. We present a modification of the fuzzy c-means algorithm, which uses the data induced metric, defined with the help of Delaunay triangulation. We detail computation o...

Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces. Aiming at the subspace clustering problem, various subspace clustering algorithms have been proposed and low rank representation based subspace clustering is a very promising and efficient subspace clustering algorithm. Low rank representation method seeks ...

data mining techniques have been used widely in the area of customer relationship management (crm). in this study, we have applied data mining techniques to address a problem in business-to-business (b2b) setting. in a manufacturer-retailer-consumer chain, a manufacturer should improve its relationship with retailers to continue its business. segmentation is a useful tool for identifying groups...