Distributed Data through Transaction Techniques a Simple and Scalable Approach

Abstract

Data mining is the automated analysis of large volumes of data looking for associations and knowledge that are implicit in data. Data mining and knowledge discovery in huge amounts of data can help from the use of parallel and distributed computational environments to improve both performance and quality of data selection

Even though new data mining techniques with improved efficiency of algorithms that could be used in single locations (some meant for multiple locations), are practiced nowadays ,the efficiency of  this methods have not focused on its practical level environment application since the data on the web/network distributed vary on its nature. This state of affairs demands the requirement for a whole new architecture and novel innovative algorithms to be used in data mining techniques. In place of long-established client server methodology new software that back up the building of a distributed data mining architecture is the need of the hour. In this paper we present software that