Abstract
Clustering for big data analytics is a growing subject due to the large size of variety data sets needed to be analyzed in distributed and parallel environment. An augmentation of K-Means clustering algorithm is projected and evaluated here for MapReduce framework by using the concepts of genetic algorithm steps. Chromosome formation, fitness calculation, optimization, and crossover logics are used to overcome the problem of suboptimal solutions of K-Means clustering algorithm and reduction of time complexity of genetic K-Means algorithm for big data. Proposed algorithm is not dealing with the selection of parents to be sent to mating pool and mutation steps, so the performance time is improved.
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Shrivastava, P., Sahoo, L., Pandey, M., Agrawal, S. (2018). AKM—Augmentation of K-Means Clustering Algorithm for Big Data. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_11
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DOI: https://doi.org/10.1007/978-981-10-7566-7_11
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