The use of conventional clustering methods combined with SOM to increase the efficiency
Autor: | Robert Jarusek, Martin Kotyrba, Pavel Smolka, Eva Volna |
---|---|
Rok vydání: | 2021 |
Předmět: |
0209 industrial biotechnology
Process (engineering) Computer science Suite 02 engineering and technology Hybrid approach computer.software_genre Field (computer science) Set (abstract data type) 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Computational Science and Engineering 020201 artificial intelligence & image processing Data mining Benchmark data Cluster analysis computer Software |
Zdroj: | Neural Computing and Applications. 33:16519-16531 |
ISSN: | 1433-3058 0941-0643 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: | |
Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |