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The use of conventional clustering methods combined with SOM to increase the efficiency

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Abstract

This article reflects research in the field of artificial intelligence and demonstrates a higher efficiency achievement of conventional clustering methods in combination with unconventional methods. It concerns a new hybrid approach based on the SOM (Self-Organizing Maps) method. We focused on the possibility of combining SOM with other clustering methods—CLARA, CURE a K-means. Method SOM is primarily useful in the first phases of the process, where knowledge of the data is too vague. It is thus followed by the use of a selected clustering algorithm. It then works with preprocessed data. Its performance, compared with its outputs on unprocessed data, is more efficient, which is proved by the performed experimental study on the benchmark data set Fundamental Clustering Problems Suite (FCPS). Part of the experimental verification was also a comparison of the achieved outputs with other approaches using this dataset based on a standard metrics—Rand index.

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Abbreviations

αs:

Shrinking factor (CURE), learning parameter (SOM)

ε :

Radius

μ :

Learning parameter

ρ :

Surroundings of the winning neuron (SOM)

D ( j ) :

Euclidean distance

BIRCH :

Balanced Iterative Reducing and Clustering using Hierarchies

CURE :

Clustering Using REpresentatives

CLARA :

Clustering LARge Applications

CLARANS :

Clustering Large Applications based on RANdomized Search

CLIQUE :

CLustering In QUEst

DBSCAN :

Density-Based Spatial Clustering of Applications with Noise

DENCLUE :

DENsity-based CLUstEring

EFCM :

Extended Fuzzy C-Means

FCPS :

Fundamental Clustering Problems Suite

MinPts :

Minimum number of other objects

MLP :

Multilayer perceptron

OPTICS :

Ordering points to identify the clustering structure

PAM :

Partitioning Around Medoids

SEEFC :

Self-organizing-map based extended fuzzy c-means

SOM :

Self-Organizing Maps

STING :

STatistical INformation Grid

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Funding

This works was supported by TACR, project no. TL02000313 and also by University of Ostrava grant SGS05/PrF/2020.

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Authors and Affiliations

  1. Department of Informatics and Computers, University of Ostrava, 30. dubna 22, 70103, Ostrava, Czech Republic

    Martin Kotyrba, Eva Volna, Robert Jarusek & Pavel Smolka

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  1. Martin Kotyrba
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  2. Eva Volna
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  3. Robert Jarusek
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Correspondence to Eva Volna.

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Kotyrba, M., Volna, E., Jarusek, R. et al. The use of conventional clustering methods combined with SOM to increase the efficiency. Neural Comput & Applic 33, 16519–16531 (2021). https://doi.org/10.1007/s00521-021-06251-9

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  • DOI: https://doi.org/10.1007/s00521-021-06251-9

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