Evolutionary approaches to relational data clustering

Author(s)

Danilo Horta

• Document type: Master's Dissertation
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2010-02-22

Examining board members

Ricardo José Gabrielli Barreto Campello; Katti Faceli; Solange Oliveira Rezende

• Advisor: Ricardo José Gabrielli Barreto Campello

Abstract

Data clustering is a fundamental technique for applications in several fields of science and marketing, as commerce, biology, psychiatry, astronomy, and Web mining. However, in a subset of these fields, such as industrial engineering, social sciences, earthquake engineering, and retrieval of documents, datasets are usually described only by proximities between their objects (called relational datasets). Even in applications where the data are not naturally relational, the use of relational datasets preserves the datas secrecy, which can be of great value to banks or brokers, for instance. This dissertation presents a review of data clustering algorithms which deals with relational datasets, with a focus on algorithms that produce hard or crisp partitions of data. Particular emphasis is given to evolutionary algorithms, which have proved of being able to solve problems of data clustering accurately and efficiently. In this context, we propose a new evolutionary algorithm for clustering able to operate on relational datasets and also able to automatically estimate the number of clusters (which is usually unknown in practical applications). It is empirically shown that this new algorithm can overcome traditional methods described in the literature in terms of computational efficiency and accuracy (AU)

• FAPESP's process: 08/00932-2 - Evolutionary Approaches for Relational Data Clustering
• Grantee: Danilo Horta
• Support Opportunities: Scholarships in Brazil - Master