NDLI: eVQ-AM: An extended dynamic version of evolving vector quantization

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2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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eVQ-AM: An extended dynamic version of evolving vector quantization

Content Provider	IEEE Xplore Digital Library
Author	Lughofer, E.
Copyright Year	2013
Description	Author affiliation: Dept. of Knowledge-Based Math. Syst., Johannes Kepler Univ. of Linz, Linz, Austria (Lughofer, E.)
Abstract	In this paper, we are presenting a new dynamically evolving clustering approach which extends conventional evolving Vector Quantization (eVQ), successfully applied before as fast learning engine for evolving cluster models, classifiers and evolving fuzzy systems in various real-world applications. The first extension concerns the ability to extract ellipsoidal prototype-based clusters in arbitrary position, thus increasing its flexibility to model any orentiation/rotation of local data clouds. The second extension includes a single-pass merging strategy in order to resolve unnecessary overlaps or to dynamically compensate inappropriately chosen learning parameters (which may lead to over-clustering effects). The new approach, termed as eVQ-AM (eVQ for Arbitrary ellipsoids with Merging functionality), is compared with conventional eVQ, other incremental and batch learning clustering methods based on two-dimensional as well as high-dimensional streaming clustering showing an evolving behavior in terms of adding/joining clusters as well as feature range expansions. The comparison includes a sensitivity analysis on the learning parameters and observations of finally achieved cluster partition qualities.
Starting Page	40
Ending Page	47
File Size	1400799
Page Count	8
File Format	PDF
ISBN	9781467358552
DOI	10.1109/EAIS.2013.6604103
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	EVQ Incremental learning Shape Merging Prototype-based clusters in arbitrary position Single-pass merging Data models Evolving clustering Data mining Covariance matrices Intelligent systems Ellipsoids
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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eVQ-AM: An extended dynamic version of evolving vector quantization