Multi-population based nature-inspired optimization algorithms have attracted wide research
interests in the last decade, and become one of the frequently used methods to handle real …

Task schedule optimization enables to attain high performance in both homogeneous and
heterogeneous computing environments. The primary objective of task scheduling is to …

The time needed to apply a hierarchical clustering algorithm is most often dominated by the
number of computations of a pairwise dissimilarity measure. Such a constraint, for larger …

In this article, a novel work pattern for large-scale workpiece machining, that is, multimobile
robot cluster machining, is proposed. The key idea is to use distributed coordination control …

Association rule mining is a popular data mining task, which has important in many domains.
Because the task of association rule mining is very time consuming, evolutionary and swarm …

Frequent itemsets mining is an active research problem in the domain of data mining and
knowledge discovery. With the advances in database technology and an exponential …

Co-clustering refers to the simultaneous clustering of objects and their features. It is used as
a clustering technique when the data exhibit similarities only in a subset of features instead …

Although 'staycation'has become a buzzword during the COVID-19 pandemic, research on
staycation experiences is still limited. This paper conducts a fine-grained sentiment analysis …

Automatic data clustering, whose goal is to recover the proper number of clusters as well as
appropriate partitioning of data sets, is a fundamental yet challenging problem in …

This work addresses the problem of clustering large uncertain graphs. The data is
represented as a graph where the proposed solution uses the neighborhood information for …