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Improving Performance of Distributed Data Mining

Recently the growth of data has created a challenge that requires higher computational power and powerful resources in order to efficiently and effectively mine information from it. The emergence of distributed and parallel systems became an active solution to the problem of large scale data mining. Parallel computing divides the large task into smaller subtasks that are executed over several nodes. Based on the concept of distributed and parallel systems, the processing of all tasks is performed concurrently and transparently. Handling large scale data mining can be performed using the MapReduce algorithm which splits a large dataset into smaller subsets. The processing is then carried out in a completely parallel mechanism which is the role of the map procedure. Another solution is the use of distributed processing and applying the concept of parallelism. This involves the division of a large dataset manually into subsets, and a number of machine learning algorithms are then applied on each. The outputs are combined in order to obtain the final result. It is possible to implement the mining process of large scale data over a central computing node, but it would be very costly in terms of time and hardware resources. The goal of this paper is to improve the performance of data mining algorithms over a large scale dataset and show a comparison between the results of distributed and parallel, with the centralized manner. One large scale dataset is used for data classification problem and three different algorithms, Random Forest (RF) in the centralized classification, Decision Tree (DT), and Random Tree (RT) in the distributed/parallel classification. The results show improvement in the performance using distributed classification in terms of execution time.