DBSCAN is a popular method for clustering multi-dimensional objects. Just as notable as
the method's vast success is the research community's quest for its efficient computation …

The freedom and transparency of information flow on the Internet has heightened concerns
of privacy. Given a set of data items, clustering algorithms group similar items together …

The development of e-systems has given consumers and businesses access to a plethora of
information, which has complicated the process of decision making. Document classification …

This paper describes a technique for clustering homogeneously distributed data in a peer-to-
peer environment like sensor networks. The proposed technique is based on the principles …

Data mining has been a popular research area for more than a decade due to its vast
spectrum of applications. However, the popularity and wide availability of data mining tools …

An ensemble of clustering solutions or partitions may be generated for a number of reasons.
If the data set is very large, clustering may be done on tractable size disjoint subsets. The …

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of
distributed data, where DDM discovers patterns and implements prediction based on …

DBSCAN is a method proposed in 1996 for clustering multi-dimensional points, and has
received extensive applications. Its computational hardness is still unsolved to this date. The …

Multi-agent systems (MAS) offer an architecture for distributed problem solving. Distributed
data mining (DDM) algorithms focus on one class of such distributed problem solving tasks …

A promising research area that has recently emerged, is on how to use index coding to
improve the communication efficiency in distributed computing systems, especially for data …