Abstract
Parallel data mining algorithms are extensively used to mine and discover hidden knowledge from varied, unrelated data. Parallel data mining algorithms provide advantages such as reduced training time, less execution time, and less memory requirement. There are several issues in executing parallel data mining algorithms in a distributed environment. It is crucial to partition the data among processors such that there is minimal data dependency, proper synchronization, communication overhead, work load balancing among nodes in distributed processors and disk IO cost. Few of these issues can be resolved when parallel data mining algorithms are executed on Apache framework called Hadoop Map Reduce. Hadoop Map Reduce provides improved performance, reduced communication cost, reduced execution time, reduced training time, and reduced IO access. This paper proposes a novel framework that aims at enhancing the aforementioned advantages in terms of scalability by increasing the number of nodes in the Hadoop cluster and analyzing the performance of classification algorithms like K-Nearest Neighbor, Naïve Bayes and Decision Tree. This parallel framework could be extended to other fields of biotechnology where prediction on large datasets is essential.
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Lokeswari, Y.V., Jacob, S.G., Ramadoss, R. (2019). Parallel Prediction Algorithms for Heterogeneous Data: A Case Study with Real-Time Big Datasets. In: Peter, J., Alavi, A., Javadi, B. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 750. Springer, Singapore. https://doi.org/10.1007/978-981-13-1882-5_46
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DOI: https://doi.org/10.1007/978-981-13-1882-5_46
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