An Approach to Clustering Models Estimation

There are numerous clustering algorithms and clustering quality criteria present at the moment. According to clustering tasks, different criteria can be used as a ground for choosing one or another clustering model. But choosing of clustering quality criteria is in turn not based on any formal appro...

Full description

Saved in:
Bibliographic Details
Published in2018 22nd Conference of Open Innovations Association (FRUCT) Vol. 426; no. 22; pp. 19 - 24
Main Authors Baimuratov, Ildar R., Zhukova, Nataly A.
Format Conference Proceeding Journal Article
LanguageEnglish
Published FRUCT Oy 01.05.2018
FRUCT
Subjects
Adjusted mutual information
Calinski-Harabasz index
Cluster analysis
Clustering algorithms
Entropy
Estimation
Indexes
Information theory
K-means
Kullback-Leibler divergence
Partitioning algorithms
Probability distribution
Systematics
Task analysis
Online AccessGet full text

Cover

More Information
Summary:There are numerous clustering algorithms and clustering quality criteria present at the moment. According to clustering tasks, different criteria can be used as a ground for choosing one or another clustering model. But choosing of clustering quality criteria is in turn not based on any formal approach and depends only on an analyst's intuition. We propose a systematic approach to clustering quality estimation by analyzing the structure of clustering models and extracting the factors of clustering quality. Further, we believe that clustering quality estimation requires estimation of knowledge or information produced in result of clustering. However, we show that existing information criteria can not be used as criteria for choosing clustering models and propose a new information criterion for clustering and examine it experimentally.
ISSN:2305-7254
2305-7254
2343-0737
DOI:10.23919/FRUCT.2018.8468286