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Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data

Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data

Ch. Vidyadhari, N. Sandhya, P. Premchand
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 21
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799805748|DOI: 10.4018/IJACI.2020100105
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MLA

Vidyadhari, Ch., et al. "Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data." IJACI vol.11, no.4 2020: pp.72-92. http://doi.org/10.4018/IJACI.2020100105

APA

Vidyadhari, C., Sandhya, N., & Premchand, P. (2020). Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data. International Journal of Ambient Computing and Intelligence (IJACI), 11(4), 72-92. http://doi.org/10.4018/IJACI.2020100105

Chicago

Vidyadhari, Ch., N. Sandhya, and P. Premchand. "Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data," International Journal of Ambient Computing and Intelligence (IJACI) 11, no.4: 72-92. http://doi.org/10.4018/IJACI.2020100105

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Abstract

In this research paper, an incremental clustering approach-enabled MapReduce framework is implemented that include two phases, mapper and reducer phase. In the mapper phase, there are two processes, pre-processing and feature extraction. Once the input data is pre-processed, the feature extraction is done using wordnet features. Then, the features are fed to the reducer phase, where the features are selected using entropy function. Then, the automatic incremental clustering is done using bat-grey wolf optimizer (BAGWO). BAGWO is the integration of bat algorithm (BA) into grey wolf optimization (GWO) for generating various clusters of text documents. Upon the arrival of the incremental data, the mapping of the new data with respect to the centroids is done to obtain the effective cluster. For mapping, kernel-based deep point distance and for centroid update, fuzzy concept is used. The performance of the proposed framework outperformed the existing techniques using rand coefficient, Jaccard coefficient, and clustering accuracy with maximal values 0.921, 0.920, and 0.95, respectively.

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