OCA: overlapping clustering application unsupervised approach for data analysis

Alvincent Egonia Danganan (Tarlac State University Technological Institute of the Philippines)
Ariel M. Sison (Emilio Aguinaldo College)
Ruji P. Medina (Technological Insttitute of the Philippines)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 14, No 3: June 2019

In this paper, a new data analysis tool called Overlapping Clustering Application (OCA) was presented. It was developed to identify overlapping clusters and outliers in an unsupervised manner. The main function of OCA is composed of three phases. The first phase is the detection of the abnormal values(outliers) in the datasets using median absolute deviation. The second phase is to segment data objects into cluster using k-means algorithm. Finally, the last phase is the identification of overlapping clusters, it uses maxdist (maximum distance of data objects allowed in a cluster) as a predictor of data objects that can belong to multiple clusters.  Experimental results revealed that the developed OCA proved its capability in detecting overlapping clusters and outliers accordingly.