Abstract
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.
Highlights
The big volume of data that are collected by an firms or individual has urge the researchers to explore more techniques to analyze this data
Genetic Algorithms (GAs) have advantage for clustering task, where it can solve the number of clusters and allocate these items to its cluster solve simultaneously [4]
The following operators are the essential process in GAs as implementation of Genetic Clustering for Unknown K (GCUK) to deal with binary data sets
Summary
The big volume of data that are collected by an firms or individual has urge the researchers to explore more techniques to analyze this data. The validation of clustering is difficult because the number of cluster K is not known prior It can be a challenging problem for a researcher to find a suitable K for the data set [1]. This well-known partitioning algorithm uses an iterative process to cluster the data It is using a single point as their searching space, which makes it to stuck in local optima [3]. To overcome this problem a promising technique namely Genetic Algorithms (GAs) was used in this study. Hamming distance was used as an acceptable measure on binary points
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