Correlation Clustering was defined by Bansal, Blum, and Chawla as the problem of clustering a set of elements based on a possibly inconsistent binary similarity function between element pairs. Their setting is agnostic in the sense that a ground truth clustering is not assumed to exist, and the only reasonable way to measure the cost of a solution is by comparing it with the input similarity fu...

Molecular epidemiologic studies of infectious diseases rely on pathogen genotype comparisons, which usually yield patterns comprising sets of DNA fragments (DNA fingerprints). We use a highly developed genotyping system, IS6110-based restriction fragment length polymorphism analysis of Mycobacterium tuberculosis, to develop a computational method that automates comparison of large numbers of fi...

The goal of cluster analysis is to separate a set of objects into constituent groups so that the members of any one group diier from one another as little as possible, according to a given criteria 6]. Pal et al. 5] proposed a generalized learning vector quantiza-tion (GLVQ) algorithm and compared it with learning vector quantization (LVQ) algorithm on clustering Anderson's IRIS data 3]. In thi...

The weighted fuzzy c-mean clustering algorithm (WFCM) and weighted fuzzy c-mean-adaptive cluster number (WFCM-AC) are extension of traditional fuzzy c-mean algorithm to stream data clustering algorithm. Clusters in WFCM are generated by renewing the centers of weighted cluster by iteration. On the other hand, WFCM-AC generates clusters by applying WFCM on the data & selecting best K± initialize...

Approximation structuring clustering is an extension of what is usually called \square-error clustering" onto various cluster structures and data formats. It appears to be not only a mathematical device to support, specify and extend many clustering techniques, but also a framework for mathematical analysis of interrelations among the techniques and their relations to other concepts and problem...

The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. Two algorithms are found in the literature and software, both announcing that they implement the Ward clustering method. When applied to the same distance matrix, they produce different results. One algori...

An approach for invariant clustering and recognition of objects (situation) in dynamic environment is proposed. This approach is based on the combination of clustering by using unsupervised neural network (in particular ART-2) and preprocessing of sensor information by using forward multilayer perceptron (MLP) with error back propagation (EBP) which supervised by clustering neural network. Usin...

Abstract. We focus on clustering gene expression temporal profiles, and propose a novel, simple algorithm that is powerful enough to find an efficient distribution of genes over clusters. We also introduce a variant of a clustering index that can effectively decide upon the optimal number of clusters for a given dataset. The clustering method is based on a profilealignment approach, which minim...

This work presents document clustering experiments performed over noisy texts (i.e. text that have been extracted through an automatic process like speech or character recognition). The effect of recognition errors on different clustering techniques is measured through the comparison of the results obtained with clean (manually typed texts) and noisy (automatic speech transcripts affected by 30...

We present an Outlier Removal Clustering (ORC) algorithm that provides outlier detection and data clustering simultaneously. The method employs both clustering and outlier discovery to improve estimation of the centroids of the generative distribution. The proposed algorithm consists of two stages. The first stage consist of purely K-means process, while the second stage iteratively removes the...