High throughput biological data need to be processed, analyzed, and interpreted to address
problems in life sciences. Bioinformatics, computational biology, and systems biology deal …

Parallel machine scheduling problems with common servers have many industrial
applications. In this paper, we study a generalized problem of scheduling with a common …

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 …

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 problem is an unsupervised learning problem. It is a procedure that partition data
objects into matching clusters. The data objects in the same cluster are quite similar to each …

The flower pollination algorithm (FPA) is a relatively new swarm optimization algorithm that
inspired by the pollination phenomenon of natural phanerogam. Since its proposed, it has …

Clustering algorithms are developed as a powerful tool to analyze the massive amount of
data which are produced by modern applications. The main goal of these algorithms is to …

Clustering is an unsupervised learning procedure and there is no a prior knowledge of data
distribution. It organizes a set of objects/data into similar groups called clusters, and the …

The marine traffic has been significantly rising fast during the last period of time. One of the
most important problems for prediction of the marine traffic is to find certain patterns …

Clustering is a very popular data analysis and data mining technique. K-means is one of the
most popular methods for clustering. Although K-mean is easy to implement and works fast …