Mini-models based on soft clustering methods | Zendy

Marcin Pietrzykowski | Zendy

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Mini-models based on soft clustering methods

Author(s) -

Marcin Pietrzykowski

Publication year - 2019

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2019.09.426

Subject(s) - computer science , cluster analysis , fuzzy clustering , centroid , artificial intelligence , data mining , feature (linguistics) , point (geometry) , fuzzy logic , mixture model , pattern recognition (psychology) , algorithm , machine learning , mathematics , philosophy , linguistics , geometry

The paper introduces new version of the mini-model method (MM-method) based on soft clustering algorithms. The article explores MM based on: Gaussian mixture models and fuzzy c-means algorithm. First method performs distribution-based clustering. The second one is fuzzy method which belongs to the centroid-based algorithms. A common feature of both methods is possibility to assign a data point to more than one cluster. The MM-method is an instance-based learning algorithm and performs local regression. The introduced method will be analyzed in several variants and results will be compared with previous versions of the MM-method and other instance-based learning algorithms.

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