Title

Pattern-based clustering using unsupervised decision trees

Author

ANDRES EDUARDO GUTIERREZ RODRÍGUEZ

Access level

Open Access

Summary or description

In clustering, providing an explanation of the results is an important task.

Pattern-based clustering algorithms provide, in addition to the list of objects

belonging to each cluster, an explanation of the results in terms of a set of

patterns that describe the objects grouped in each cluster. It makes these

algorithms very attractive from the practical point of view; however, patternbased

clustering algorithms commonly have a high computational cost in the

clustering stage. Moreover, the most recent algorithms proposed within this

approach, extract patterns from numerical datasets by applying an a priori

discretization process, which may cause information loss. In this thesis, we

propose new algorithms for extracting only a subset of patterns useful for

clustering, from a collection of diverse unsupervised decision trees induced

from a dataset. Additionally, we propose a new clustering algorithm based

on these patterns.

Publisher

Instituto Nacional de Astrofísica, Óptica y Electrónica

Publish date

November 23, 2015

Publication type

Doctoral thesis

Format

application/pdf

Language

English

Audience

General public

Citation suggestion

Gutierrez-Rodriguez A. E.

Source repository

Repositorio Institucional del INAOE

Downloads

1862

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