eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

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.

A Density-Based Method for the Identification of Non-Disjoint Clusters With Arbitrary and Non-Spherical Shapes

Overlapping clustering is an important challenge in unsupervised learning applications while it allows for each data object to belong to more than one group. Several clustering methods were proposed to deal with this requirement by using several usual clustering approaches. Although the ability of these methods to detect non-disjoint partitioning, they fail when data contain groups with arbitrary and non-spherical shapes. We propose in this work a new density based overlapping clustering method, referred to as OC-DD, which is able to detect overlapping clusters even having non-spherical and complex shapes. The proposed method is based on the density and distances to detect dense regions in data while allowing for some data objects to belong to more than one group.Experiments performed on articial and real multi-labeled datasets have shown the effectiveness of the proposed method compared to the existing ones.

Categorization of Multiple Documents Using Fuzzy Overlapping Clustering Based on Formal Concept Analysis

Most clustering algorithms build disjoint clusters. However, clusters might be overlapped because documents may belong to two or more categories in the real world. For example, a paper discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. Therefore, overlapping clustering algorithms have been studied such that a resource can be assigned to one or more clusters. Formal Concept Analysis (FCA), which has many practical applications in information science, has been used in disjoin clustering, but has not been studied in overlapping clustering. To make overlapping clustering possible by using FCA, we propose an approach, including two types of transformation. From the experimental results, it shows that the proposed fuzzy overlapping clustering performed more efficiently than existing overlapping clustering methods. The positive results confirm the feasibility of the proposed scheme used in overlapping clustering. Also, it can be used in applications such as recommendation systems.