Performance Evaluations of κ-Approximate Modal Haplotype Type Algorithms for Clustering Categorical Data

Parallel Clustering Algorithms for Categorical and Mixed Data

Clustering Ensembles for Categorical Data

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. In this paper we focus on the design of ensembles for categorical data. Our approach leverages diverse input clusterings discovered in random subspaces. We experimentally demostrate the efficacy of our technique in combination with the categorical clustering algorithm COOLCAT.

Data Reduction Method for Categorical Data Clustering

Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes...

Clustering Large Categorical Data Set via Genetic Algorithms

Riassunto: Illustriamo due estensioni − proposte da Huang (1998) e da Kauffman e Rousseew (1990) − del noto algoritmo di raggruppamento a centri mobili al caso in cui si considerino variabili qualitative. Proponiamo una nuova metodologia di raggruppamento basata sulla logica degli algoritmi genetici. L’applicazione di tali algoritmi ad un data set reale e l’analisi dei risultati ottenuti eviden...

A Comparative Study of Some Clustering Algorithms on Shape Data

Recently, some statistical studies have been done using the shape data. One of these studies is clustering shape data, which is the main topic of this paper. We are going to study some clustering algorithms on shape data and then introduce the best algorithm based on accuracy, speed, and scalability criteria. In addition, we propose a method for representing the shape data that facilitates and ...