Clustering is the process of partitioning the data objects such that the degree of similarity among the objects in the same partition is more than the degree of dissimilarity between the objects of different classes. There are many applications of clustering like medicine, marketing, insurance, image analysis etc. The squared-error clustering algorithm uses single prototype to represent a clust...

A common approach for solving clustering problems is to design algorithms to approximately optimize various objective functions (e.g., k-means or min-sum) defined in terms of some given pairwise distance or similarity information. However, in many learning motivated clustering applications (such as clustering proteins by function) there is some unknown target clustering; in such cases the pairw...

We propose and analyze a fast spectral clustering algorithm with computational complexity linear in the number of data points that is directly applicable to large-scale datasets. The algorithm combines two powerful techniques in machine learning: spectral clustering algorithms and Nyström methods commonly used to obtain good quality low rank approximations of large matrices. The proposed algori...

Clustering is a powerful tool in revealing the intrinsic organization of data. A clustering of structural patterns consists of an unsupervised association of data based on the similarity of their structures and primitives. This chapter addresses the problem of structural clustering, and presents an overview of similarity measures used in this context. The distinction between string matching and...

In a speaker veri ̄cation system, a claimed speaker's score is computed to accept or reject the speaker claim. Most of the current methods compute the score as the ratio of the claimed speaker's and the impostors' likelihood functions. Based on analysing false acceptance error obtained by using these methods, we propose a noise clustering approach to ̄nd better scores which can reduce that error....

There has been recent interest in clustering text data to build topic-specific language models for large vocabulary speech recognition. In this paper, we studied various unsupervised clustering algorithms on several corpora. First we compared the clustering methods with quality metrics such as entropy and purity. Of the techniques studied, two-phase bisecting K-means achieved good performance w...

In this paper, the proposed approach is an unique combination of two most popular clustering algorithms Particle Swarm Optimization (PSO) and K-Means to achieve better clustering result. Clustering is a technique of grouping homogeneous objects of a dataset with aim to extract some meaningful pattern or information. K-Means algorithm is the most popular clustering algorithm because of its easy ...

Most clustering algorithms assume that all dimensions of the data can be described by a single structure. Cross-clustering (or multiview clustering) allows multiple structures, each applying to a subset of the dimensions. We present a novel approach to crossclustering, based on approximating the solution to a Cross Dirichlet Process mixture (CDPM) model [Shafto et al., 2006, Mansinghka et al., ...

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...