The k-medoids problem is a combinatorial optimisation problem with multiples applications in Resource Allocation, Mobile Computing, Sensor Networks and Telecommunications. Real instances of this problem involve hundreds of thousands of points and thousands of medoids. Despite the proliferation of parallel architectures, this problem has been mostly tackled using sequential approaches. In this p...

People are always in search of matters for which they are prone to use internet, but again it has huge assemblage of data due to which it becomes difficult for the reader to get the most accurate data. To make it easier for people to gather accurate data, similar information has to be clustered at one place. There are many algorithms used for clustering of relevant information in one platform. ...

K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k medoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it generates unstable and empty clusters which are meaningless. expensive and requires time proportional to the product of the number of data items, number of clust...

◮ It can compute exact and approximate probabilities with error guarantees for the clustering output. State-of-the-art techniques (e.g. UK-means, UKmedoids, MMVar): ◮ do not support the possible worlds semantics, ◮ lack support for correlations and assume probabilistic independence, ◮ use deterministic cluster medoids or expected means, and ◮ can only compute clustering based on expected distan...

Now a days, it becomes more difficult for users to find the documents related to their interests, since the number of available web pages grows at large. Clustering is the method of grouping the data objects into classes or clusters so that data objects within a cluster have high similarity as compared to one another, but are very dissimilar to objects in other clusters. Such similarity measure...

Clustering is one of the data analysis methods that are widely used in data mining. In this method, we partitioned the data into different subset which is known as cluster. Cluster analysis is the data reduction toll for classifying a “mountain‟ of information into manageable meaningful piles. This method is vast research area in the field of data mining. In this paper, a partitioning clusterin...

We run experiments showing that algorithm clarans (Ng et al., 2005) finds better Kmedoids solutions than the standard algorithm. This finding, along with the similarity between the standard K-medoids and K-means algorithms, suggests that clarans may be an effective K-means initializer. We show that this is the case, with clarans outperforming other popular seeding algorithms on 23/23 datasets w...