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Published in:
2010 | OriginalPaper | Chapter
Parallel Ant-Miner (PAM) on High Performance Clusters
Authors : Janaki Chintalapati, M. Arvind, S. Priyanka, N. Mangala, Jayaraman Valadi
Published in: Swarm, Evolutionary, and Memetic Computing
Publisher: Springer Berlin Heidelberg
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This study implements parallelization of Ant-Miner for classification rules discovery. Ant-Miner code is parallelized and optimized in a cluster environment by employing master-slave model. The parallelization is achieved in two different operations of Ant-Miner viz. discretization of continuous attributes and rule construction by ants. For rule mining operation, ants are equally distributed into groups and sent across the different cluster nodes. The performance study of Parallel Ant-Miner (PAM) employs different publicly available datasets. The results indicate remarkable improvement in computational time without compromising on the classification accuracy and quality of discovered rules. Dermatology data having 33 features and musk data having 168 features were taken to study performance with respect to timings. Speedup almost equivalent to ideal speedup was obtained on 8 CPUs with increase in number of features and number of ants. Also performance with respect to accuracies was done using lung cancer data.