eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

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.

Performance of K-means algorithm based an ensemble learning

“…Therefore, running the algorithm multiple times, different compilation results can be obtained each time, depending on initial centroid. Different solutions have been proposed to solve the algorithm problems [18], [19].…”

Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering

“…26, No. 3, June 2022: 1607-1615 1608 analysis, and information retrieval [6]. Clustering serves as a significant area in data mining applications and data analysis.…”

“…Outlier detection remains to be a research branch in data mining which plays a significant and extensive role because of its widespread use in a wide range of applications [8]. The outlier is the data item whose value falls outside the bounds in the sample data may indicate anomalous data [6]. Moreover, it is a data item whose values vary from the rest of the data or whose values fall outside the described range [9].…”