Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

Cluster ensemble has emerged as a powerful technique for combining multiple clustering
results. To address the problem of clustering on large-scale data, this paper presents an …

In human concept learning, people can naturally combine a handful of labeled data with
abundant unlabeled data when they make classification decisions, which is also known as …

Clustering is an important research direction in data mining. However, there is no one
clustering algorithm that can be applied efficiently in all situation. Clustering ensemble is the …

Hadoop provides an environment for efficient storage and processing of data. Time for
completion of a BigData job depends on the slowest mapper or slowest reducer. So for an …

Remote Access Trojans (RATs) provide cyber criminals with unlimited access to infected
endpoints. Using the victim's access privileges, they can access and steal sensitive business …

Semi-supervised consensus clustering is a promising strategy to compensate for the
subjectivity of clustering and its sensitivity to design factors, with various techniques being …

Clustering, as a fundamental exploratory data technique, not only is used to discover
patterns and structures in complex datasets but also is utilized to group variables in high …

A keyword search on constrained clustering on Web-of-Science returned just under 3,000
documents. We ran automatic analyses of those, and compiled our own bibliography of 183 …

Although clustering with pairwise constraints through penalty regularization has been widely
adopted in existing semi-supervised clustering approaches, little work has been done on …