Many datasets including social media data and bibliographic can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of data. To improve quality clustering, node attributes taken account, resulting in attributed Existing graph clustering methods generally consider attribute similarity structural separately. In this paper, we represent star-schema he...

Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent grouping of the text documents.The importance of document clustering emerges from the massive volumes of textual documents created. Also, with more and more development of information technology, data set in many domains is reaching beyond pe...

Clustering large data is a fundamental problem with a vast number of applications. Due to the increasing size of data, practitioners interested in clustering have turned to distributed computation methods. In this work, we consider the widely used kcenter clustering problem and its variant used to handle noisy data, k-center with outliers. In the noise-free setting we demonstrate how a previous...

Recent advances in information technology have led to an increase in volumes of data thereby exceeding beyond petabytes. Clustering distributed document sets from a central location is difficult due to the massive demand of computational resources. So there is a need for distributed document clustering algorithms to cluster documents using distributed resources. The greatest challenge in this a...

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—analysis and modeling of distributed data. This paper offers a perspective on DDM algorithms in the context of multiagents systems. It discusses broadly the connection between DDM and MAS. It provides a high-level ...