Parallelizing dynamic scientiic applications involves solving the dynamic load balancing problem. The balancing should take the communication requirements of the application into account. Many problems are dealing with objects in k-dimensional space with very special communication patterns. We describe a kind of an orthogonal recursive bisection clustering and show that its dynamic adaption inv...

Clustering domain is vital part of data mining domain and widely used in different applications. In this project we are focusing on affinity propagation (AP) clustering which is presented recently to overcome many clustering problems in different clustering applications. Many clustering applications are based on static data. AP clustering approach is supporting only static data applications, he...

Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimens...

A Wireless sensor network is an infrastructure comprised of sensing (measuring), computing, and communication elements that gives an administrator the ability to instrument, observe, and react to events and phenomena in a specified environment. The administrator typically is a civil, governmental, commercial, or industrial entity. The environment can be the physical world, a biological system, ...

Graph clustering is concerned with identifying the group structure of networks. Existing static methods, however, are not the best choice for evolving networks with evolving group structures. We discuss dynamic versions of existing clustering algorithms which maintain and modify a clustering over time rather than recompute it from scratch. We developed an extensible software framework for the e...