Partitional data clustering with K-means algorithm is the dividing of objects into smaller and
disjoint groups that has the most similarity with objects in a group and most dissimilarity from …

Clustering is the process of grouping data objects into set of disjoint classes called clusters
so that objects within a class are highly similar with one another and dissimilar with the …

It is well known that k-means algorithm is one of the most widely used clustering techniques.
However, solutions of k-means algorithm depend on the initialization of cluster centers and …

Data clustering helps one discern the structure of and simplify the complexity of massive
quantities of data. It is a common technique for statistical data analysis and is used in many …

K-Means clustering algorithm attracts increasing focus in recent years. A pending problem of
K-Means clustering algorithm is that the performance is affected by the original cluster …

In this paper, we proposed two hybrid data clustering algorithms that are called ICAFKM and
PSOFKM. ICAFKM combined the advantageous aspects of Fuzzy K-Means (FKM) and …

Clustering is used to group data objects into sets of disjoint classes called clusters so that
objects within the same class are highly similar to each other and dissimilar from the objects …

Clustering is an unsupervised kind of grouping of data points based on the similarity that
exists between them. This paper applied a combination of particle swarm optimization and K …

Cluster Analysis is a popular data analysis in data mining technique. Clusters play a vital
role for users to organize, summarize and navigate the data effectively. Swarm Intelligence …

Data clustering is a technique for clustering set of objects into known number of groups.
Several approaches are widely applied to data clustering so that objects within the clusters …