An Efficient Density based Improved K- Medoids Clustering algorithm

Abstract

Abstract-Clustering is the process of classifying objects into different groups by partitioning sets of data into a series of subsets called clusters. Clustering has taken its roots from algorithms like k-medoids and k-medoids. However conventional k-medoids clustering algorithm suffers from many limitations. Firstly, it needs to have prior knowledge about the number of cluster parameter k. Secondly, it also initially needs to make random selection of k representative objects and if these initial k medoids are not selected properly then natural cluster may not be obtained. Thirdly, it is also sensitive to the order of input dataset. Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The database can be clustered in many ways depending on the clustering algorithm employed, parameter settings used, and other factors. Multiple clustering can be combined so that the final partitioning of data provides better clustering. In this paper, an efficient density based k-medoids clustering algorithm has been proposed to overcome the drawbacks of DBSCAN and kmedoids clustering algorithms. The result will be an improved version of kmedoids clustering algorithm. This algorithm will perform better than DBSCAN while handling clusters of circularly distributed data points and slightly overlapped clusters.

Link Prediction in Social Networks based on Spectral Clustering using k-medoids and Landmark

“…k-medoids Algorithm which is also known as Partition Around Medoids (PAM) is developed by Kaufman and Rousseuw in 1987 [22][23] [24]. k-medoids algorithm is comparatively robust than k-means particularly in the context of outliers and noise because the k-means algorithm is sensetive to outliers.…”

“…Because a mean is sensetive to the outliers, i.e., if you look at an ages of students in one class, if you adding another very high age, the average age of the whole class shifts alot. So instead of taking the mean value of object in a cluster as a centroid the most centrally located object is used in the cluster as a medoids in k-medoids [22] [24]. k-medoids is a classical partitioning technique of clustering that clusters the data set of m objects into k number of clusters [24].…”

“…So instead of taking the mean value of object in a cluster as a centroid the most centrally located object is used in the cluster as a medoids in k-medoids [22] [24]. k-medoids is a classical partitioning technique of clustering that clusters the data set of m objects into k number of clusters [24]. The user gives the number of clusters required.…”

Document Classification Using Enhanced Grid Based Clustering Algorithm

“…An efficient density based K-medoids clustering algorithm has been proposed to overcome the drawbacks of DBSCAN and K-medoids clustering algorithms [1].…”

“…According to the for mentioned related works [1], used Kmediods to improve the performance of DBSCAN. Whereas [2] used a popular algorithm which is SVM, one of the drawbacks of this algorithm is the long training time.…”