L.A. Rasyid, S. Andayani
Clustering groups a set of data object into clusters to maximize the similarity between objects in the same cluster and the dissimilarity between objects in the different clusters. Clustering has been applied in many sectors such as business intelligence, image pattern recognition, web search, biology, and security. The different kinds of application trigger the emergence of many clustering algorithms and make the importance of classification to distinguish them. The aim of this study is to describe the classification of clustering algorithms based on the processed data type and to find the clustering algorithm working on different data type, i.e. the combination of numeric and fuzzy linguistic. The study is conducted by surveying several earlier researches on clustering algorithms. The result shows that each clustering algorithm works on certain data type such as numerical, categorical, multivariate, or spatial, but lack in the combination of numeric and fuzzy linguistic data. The differences, the advantages as well as disadvantages of each algorithm and the opportunity to develop a clustering approach for combination of numeric and fuzzy linguistic are also discussed. © 2018 Published under licence by IOP Publishing Ltd.
Mathematics Study Program, Universitas Negeri Yogyakarta, Jl. Colombo No. 1, Indonesia; Karangmalang Sleman, Yogyakarta, Indonesia
