While data mining has its roots in the traditional fields of machine learning and statistics, the total volume of data mostly poses the most serious problem for which many organizations have data warehouses. Implementation of data mining ideas in highperformance parallel and distributed computing environments is thus becoming crucial for ensuring system scalability and interactivity as data continues to grow relentlessly in size and complexity. The large set of evolving and distributed data can be handled efficiently by Parallel Data mining and Distributed Data Mining. In this paper we present a load balancing techniques that can deal with inter dependent task. Instead of balancing the load in cluster by process migration, or by moving an entire process to a less loaded computer, we make an attempt to balance load by splitting processes into separate jobs and then balance them to nodes.

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Heterogeneous cluster Dynamic load balancing distributed systems Parallel data mining Distributed data mining