Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)

Merging Clusters in Summary Structures for Data Stream Mining based on Fuzzy Similarity Measures

Authors
Leonardo Schick, Priscilla Lopes, Heloisa Camargo
Corresponding Author
Leonardo Schick
Available Online August 2019.
DOI
10.2991/eusflat-19.2019.111How to use a DOI?
Keywords
Data stream clustering Data summary Fuzzy similarity measures Fuzzy clustering.
Abstract

Fuzzy Clustering is one of the mining techniques that have been used to extract information from Data Streams. In our previous research we have developed an algorithm called d-FuzzStream, a fuzzy version of the Online-Offline Framework, which consists of two steps: an online step, where a summary structure formed by fuzzy micro-clusters is built and an offline step, where the micro clusters are clustered in batch mode. The quality of the data summary depends on the criteria used to decide whether an example starts a new micro-cluster or is absorbed by the existing ones; and whether two micro clusters became similar enough to be merged. In d-FuzzStream algorithm such decisions are based on concepts of fuzzy dispersion and a distance-based fuzzy clusters similarity. In this paper we investigate the behavior of different fuzzy similarity measures on the decision of merging two fuzzy micro-clusters during the online step. Experiments were run using five synthetic data sets and four fuzzy similarity measures. The results obtained are analyzed and discussed through informative and purity measures.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
Series
Atlantis Studies in Uncertainty Modelling
Publication Date
August 2019
ISBN
978-94-6252-770-6
ISSN
2589-6644
DOI
10.2991/eusflat-19.2019.111How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Leonardo Schick
AU  - Priscilla Lopes
AU  - Heloisa Camargo
PY  - 2019/08
DA  - 2019/08
TI  - Merging Clusters in Summary Structures for Data Stream Mining based on Fuzzy Similarity Measures
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 812
EP  - 819
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.111
DO  - 10.2991/eusflat-19.2019.111
ID  - Schick2019/08
ER  -