logo SBA

ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-07062007-185828


Thesis type
Tesi di dottorato di ricerca
Author
Cimino, Mario Giovanni Cosimo Antonio
URN
etd-07062007-185828
Thesis title
CLUSTERING IN NON-METRIC SPACES
Academic discipline
ING-INF/05
Course of study
INGEGNERIA DELL'INFORMAZIONE
Supervisors
Relatore Prof. Marcelloni, Francesco
Relatore Prof.ssa Lazzerini, Beatrice
Keywords
  • CLUSTER VALIDITY
  • CONDITIONAL CLUSTERING
  • FUZZY CLUSTERING
  • FUZZY IDENTIFICATION
  • FUZZY INTERPRETABILITY
  • GENETIC ALGORITHMS
  • NETWORKS
  • NEURAL NETWORKS
  • NEURAL RECEPTIVE FIELDS
  • NOISE PROTOTIPE
  • RADIAL BASIS FUNCTION (RBF)
  • RELATIONAL CLUSTERING
  • ROBUST FUZZY RELATIONAL CLUSTERING
  • SIMILARITY LEARNING
  • SIMILARITY RELATION
  • TAKAGI-SUGENO MODEL
Graduation session start date
25/05/2007
Availability
Withheld
Release date
25/05/2047
Summary
ONE OF THE CRITICAL ASPECTS OF CLUSTERING ALGORITHMS IS THE CORRECT IDENTIFICATION OF THE DISSIMILARITY MEASURE USED TO DRIVE THE PARTITIONING OF THE DATA SET. THE DISSIMILARITY MEASURE INDUCES THE CLUSTER SHAPE AND THEREFORE DETERMINES THE SUCCESS OF CLUSTERING ALGORITHMS. AS CLUSTER SHAPES CHANGE FROM A DATA SET TO ANOTHER, DISSIMILARITY MEASURES SHOULD BE EXTRACTED FROM DATA. TO THIS AIM, WE EXPLOIT SOME PAIRS OF POINTS WITH KNOWN DISSIMILARITYVALUETO LEARN ADISSIMILARITYMEASURE.THEN,WEUSETHEDISSIMILARITYMEASURETO GUIDE AN UNSUPERVISED FUZZY RELATIONAL CLUSTERING ALGORITHM. WE APPLY AND COMPARE TWO DIFFERENT METHODS FOR DISSIMILARITY EXTRACTION ON BOTH SYNTHETIC AND REAL DATA SETS. FURTHER, W E DISCUSS THE ADVANTAGES OF USING A NOVEL APPROACH, RECENTLY PROPOSED BY THE AUTHORS, TO RELATIONAL CLUSTERING THAT PARTITIONS THE DATA SET BASED ON THE PROXIMITY OF THE VECTORS CONTAINING THE DISSIMILARITY VALUES BETWEEN EACH PATTERN AND ALL THE OTHER PATTERNSIN THEDATASET. EXPERIMENTALRESULTSSHOW THAT,EVEN WITH ALOW PERCENTAGEOF KNOWN DISSIMILARITIES, THE COMBINATION LEARNING ALGORITHM/FUZZY RELATIONAL CLUSTERING ALGORITHM ALLOWSGENERATING TRUTHFULPARTITIONSOFTHEDATASETS.
File