Unsupervised Initialization of Archetypal Analysis and Proportional Membership Fuzzy Clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper further investigates and compares a method for fuzzy clustering which retrieves pure individual types from data, known as the fuzzy clustering with proportional membership (FCPM), with the FurthestSum Archetypal Analysis algorithm (FS-AA). The Anomalous Pattern (AP) initialization algorithm, an algorithm that sequentially extracts clusters one by one in a manner similar to principal component analysis, is shown to outperform the FurthestSum not only by improving the convergence of FCPM and AA algorithms but also to be able to model the number of clusters to extract from data. A study comparing nine information-theoretic validity indices and the soft ARI has shown that the soft Normalized Mutual Information max and the Adjusted Mutual Information (AMI) indices are more adequate to access the quality of FCPM and AA partitions than soft internal validity indices. The experimental study was conducted exploring a collection of 99 synthetic data sets generated from a proper data generator, the FCPM-DG, covering various dimensionalities as well as 18 benchmark data sets from machine learning.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings
EditorsHujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Place of PublicationCham
PublisherSpringer
Pages12-20
Number of pages9
ISBN (Electronic)978-3-030-33617-2
ISBN (Print)978-3-030-33616-5
DOIs
Publication statusPublished - 2019
Event20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - Manchester, United Kingdom
Duration: 14 Nov 201916 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume11872 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
CountryUnited Kingdom
CityManchester
Period14/11/1916/11/19

Keywords

  • Archetypal analysis
  • Fuzzy clustering
  • Information-Theoretic validity indices
  • Number of clusters

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