Parametric model-based clustering

Nikulin, Vladimir and Smola, Alex (2005). Parametric model-based clustering. In: Belur V. Dasarathy, Data mining, intrusion detection, information assurance and data networks security 2005: Proceedings of SPIE Vol. 5812. Data mining, intrusion detection, information assurance, and data networks security, Orlando, Florida USA, (190-201). 28-29 March 2005. doi:10.1117/12.603199

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Author Nikulin, Vladimir
Smola, Alex
Title of paper Parametric model-based clustering
Conference name Data mining, intrusion detection, information assurance, and data networks security
Conference location Orlando, Florida USA
Conference dates 28-29 March 2005
Proceedings title Data mining, intrusion detection, information assurance and data networks security 2005: Proceedings of SPIE Vol. 5812   Check publisher's open access policy
Journal name Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005   Check publisher's open access policy
Place of Publication Bellingham, Washington, U.S.A.
Publisher SPIE
Publication Year 2005
Sub-type Fully published paper
DOI 10.1117/12.603199
Open Access Status File (Publisher version)
ISBN 0819457973
9780819457974
ISSN 0277-786X
Editor Belur V. Dasarathy
Volume 5812
Start page 190
End page 201
Total pages 12
Language eng
Formatted Abstract/Summary
Parametric, model-based algorithms learn generative models from the data, with each model corresponding to one particular cluster. Accordingly, the model-based partitional algorithm will select the most suitable model for any data object (Clustering step}, and will recompute parametric models using data specifically from the corresponding clusters {Maximization step). This Clustering-Maximization framework have been widely used and have shown promising results in many applications including complex variable-length data. The paper proposes (Experience-Innovation} (EI) method as a natural extension of the (Clustering-Maximization} framework. This method includes 3 components: (1) keep the best past experience and make empirical likelihood trajectory monotonical as a result; (2) find a new model as a function of existing models so that the corresponding cluster will split existing clusters with bigger number of elements and smaller uniformity; (3) heuristical innovations, for example, several trials with random initial settings. Also, we introduce clustering regularisation based on the balanced complex of two conditions: (1) significance of any particular cluster; (2) difference between any 2 clusters. We illustrate effectiveness of the proposed methods using first-order Markov model in application to the large web-traffic dataset. The aim of the experiment is to explain and understand the way people interact with web sites.
Subjects 0199 Other Mathematical Sciences
Keyword Clustering Analysis
Mathematics
Parametric
Q-Index Code EX
Additional Notes Proceedings of SPIE--the International Society for Optical Engineering, v. 5812.

 
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