Open AccessA cluster Analysis for Binary Data Using Genetic Algorithms
Author(s) -
Sabariah Saharan,
Wong Yu Xian,
Roberto Baragona
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.30.28174
Subject(s) - cluster analysis , data mining , computer science , cure data clustering algorithm , binary data , correlation clustering , cluster (spacecraft) , clustering high dimensional data , focus (optics) , single linkage clustering , fuzzy clustering , determining the number of clusters in a data set , canopy clustering algorithm , binary number , algorithm , artificial intelligence , mathematics , physics , arithmetic , optics , programming language
This research was initially driven by the lack of clustering algorithms that focus on binary data. A promising technique to analyze this type of data, namely Genetic Clustering for Unknown K (GCUK) became the main subject in this research. GCUK was applied to cluster four binary data and there is a presence of an imbalanced data in one of the data sets. The results show that GCUK is an efficient and effective clustering algorithm compared to K-means. The other contribution is the capability of GCUK for clustering the unbalanced data. Standard clustering algorithms cannot simply be applied to this type of data sets as it can cause a misclassification results.