Clustering-based feature selection

Author(s)

Thiago Ferreira Covões

• 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

Eduardo Raul Hruschka; Nelson Francisco Favilla Ebecken; Maria Carolina Monard

• Advisor: Eduardo Raul Hruschka

Abstract

The technological progress has lead to the generation and storage of abundant amounts of data. The extraction of information from such data has required the formulation of new data analysis tools. In this context, the Knowledge Discovery from Databases process was introduced. It is focused on the identification of valid, new, potentially useful, and comprehensible patterns in large databases. In this process, the task of finding patterns in data is usually called Data Mining. The efficacy and efficiency of data mining algorithms are directly influenced by the amount and quality of the data being analyzed. Redundant and/or uninformative features may make the data mining process inefficient. In this context, feature selection methods that can remove such features are frequently used. This work proposes a feature selection algorithm and some of its variants that are capable of identifying redundant features through clustering. The identification of redundant features can favor not only the pattern recognition process but also the comprehensibility of the obtained model. The proposed method and its variants are compared with two feature selection algorithms based on feature clustering. These algorithms were evaluated in two well known data mining problems: classification and clustering. The results obtained show that the proposed algorithm obtained good accuracy and computational efficiency results, additionally not requiring the definition of critical parameters by the user (AU)

• FAPESP's process: 09/03580-2 - Clustering Based Approaches for Feature Selection
• Grantee: Thiago Ferreira Covões
• Support Opportunities: Scholarships in Brazil - Master