Experiences using clustering and generalizations for knowledge discovery in melanomas domain

A. Fornells*, E. Armengol, E. Golobardes, S. Puig, J. Malvehy

*Corresponding author for this work

Research output: Book chapterConference contributionpeer-review

5 Citations (Scopus)

Abstract

One of the main goals in prevention of cutaneous melanoma is early diagnosis and surgical excision. Dermatologists work in order to define the different skin lesion types based on dermatoscopic features to improve early detection. We propose a method called SOMEX with the aim of helping experts to improve the characterization of dermatoscopic melanoma types. SOMEX combines clustering and generalization to perform knowledge discovery. First, SOMEX uses Self-Organizing Maps to identify groups of similar melanoma. Second, SOMEX builds general descriptions of clusters applying the anti-unification concept. These descriptions can be interpreted as explanations of groups of melanomas. Experiments prove that explanations are very useful for experts to reconsider the characterization of melanoma classes.

Original languageEnglish
Title of host publicationAdvances in Data Mining
Subtitle of host publicationMedical Applications, E-Commerce, Marketing, and Theoretical Aspects - 8th Industrial Conference, ICDM 2008, Proceedings
Pages57-71
Number of pages15
DOIs
Publication statusPublished - 2008
Event8th Industrial Conference on Data Mining, ICDM 2008 - Leipzig, Germany
Duration: 16 Jul 200818 Jul 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5077 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Industrial Conference on Data Mining, ICDM 2008
Country/TerritoryGermany
CityLeipzig
Period16/07/0818/07/08

Keywords

  • Clustering
  • Dermoscopy
  • Explanations
  • Knowledge Discovery
  • Medicine
  • Melanoma
  • Self-Organizing Maps
  • Skin Tumour

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