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

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.