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

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