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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Alvincent E. Danganan
Tarlac State University
Electrical & Electronics Engineering

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eHMCOKE: an enhanced overlapping clustering algorithm for data analysis Alvincent E. Danganan; Edjie Malonzo De Los Reyes
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2547

Abstract

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.
Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering Alvincent E. Danganan; Regina P. Arceo
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1607-1615

Abstract

Most natural world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median to identify overlapping clusters in the presence of outliers. This new method aims to determine the insensitivity of the OCKMEx algorithm in locating data points that overlap even with outliers. An experimental evaluation of the algorithm was conducted wherein synthetic datasets served as a data source, and the F1 measure criterion was applied to assess the OCKMEx algorithm performance. Results indicate that the OCKMEx algorithm implementing the use of k-median performed a higher accuracy rate of 100% in identifying data points that overlap even with outliers compared to the existing k-means algorithm. The algorithm exhibited a promising performance in identifying overlapping clusters and was resistant to outliers.
Co-Authors Edjie Malonzo De Los Reyes Regina P. Arceo