NFDDC: Novel Neuro-Fuzzy Logic based Methodology for Distributed Data Classification
Shahina Parveen M
Dr. Shahina Parveen M*, Information Science Engineering Dept, CMR Institute of Technology, Bengaluru, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5370-5375 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7576118419/2019©BEIESP | DOI: 10.35940/ijrte.D7576.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the growing culture of Internet applications and their usage lead to challenging task for storing a massive volume of high-velocity data from different fields. This result an evolution of big data with integrated, i.e. Volume, Velocity, and Variety (3V’s). The voluminous data extraction is a very complex task which is not possible form classical data mining techniques. Therefore, a big data mining technique is introducing by modifying traditional data mining scheme using a novel of Neuro-Fuzzy Logic based approach, i.e. named as NFDDC. The proposed distributed data classification model performs into three stages first- reduce the data set dimension, second- data clustering, and third-data classification using the neuro-fuzzy method. The performance of the NFDDC system is analysed using two different datasets, i.e. medical data and e-commerce datasets. Additionally, comparative analysis is performed by measuring the accuracy of existing CCSA algorithm with proposed NFDDC algorithm and will get 90% accuracy in data classification.
Keywords: Distributed data Data, Data Mining, Neuro-Fuzzy Logic, Classification.
Scope of the Article: Classification.