Data Mining Techniques in Parallel Environment- A Comprehensive Survey

Abstract

Data mining is the process of discovering interesting and useful patterns and relationships in large volumes of data. The valuable knowledge can be discovered through the process of data mining for the further use and prediction. We have different data mining techniques like clustering classification and association. Classification is one of the major techniques to discover the patterns in huge amount of data. This technique is widely used in many fields. We have a large volume of data and if we extract the data sequentially then it will take a lot of timing. So if we extract the data parallely, the amount of time taken can be reduced. We can use parallel techniques when there is a large volume of data and we want to extract the data in very few seconds. We can implement this techniques using different approaches like MPI, OPENMP, using CUDA or using Map Reduce approach. Here in this paper we will discuss data mining techniques classification by decision tree induction and k-nearest neighbors using both sequential approach as well as parallel approach.