Enhancement of Classification using FPFF-ANN for Big data Analysis in Distributed Environment
K Murali Gopal1, Pragnyaban Mishra2, R. P. Singh3

1K Murali Gopal*, Computer Science and Engineering, Satya Sai University of Technical and Medical Science (SSSUTMS), Sehore, Madhya Pradesh, India.
2Dr. Pragnyaban Mishra, Computer Science and engineering, KL University, Vijayawada, India.
3Dr. R. P. Singh, Computer Science and Engineering, Satya Sai University of Technical and Medical Science (SSSUTMS), Sehore, Madhya Pradesh, India
Manuscript received on May 16, 2020. | Revised Manuscript received on June 01, 2020. | Manuscript published on June 10, 2020. | PP: 1033-1040 | Volume-9 Issue-8, June 2020. | Retrieval Number: G5712059720/2020©BEIESP | DOI: 10.35940/ijitee.G5712.069820
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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: The development of massive amount of information from any source of group at any time, wherever and from any device which is termed as Big Data. The age group of big data becomes a dangerous challenge to grip, take out and access these data is short length of time. The detection of everyday item sets is an significant issue of data mining which helps in engendering the qualitative information for the business insight and helps for the verdict makers. For the extracting the necessary item sets from the big data a variety of big data logical techniques has been evolved such as relationship rule mining, genetic algorithm, mechanism learning, FP-growth algorithm etc. In this paper we suggest FP-ANN algorithm to promote the FP enlargement calculation with neural networks to maintain the feed forward approach. The recommend algorithm uses the Twitter social dataset for the collection of frequent item sets and the proportional analysis of this approach is done using the different performance measuring parameters such as Precision, Recall, F-measure, Time complexity, Computation cost and time. The simulation of proposed work is done using the JDK, JavaBeans, and Wamp server software. The experimental results of projected algorithm gives better results in deference of time difficulty, computation cost and time also. It also gives enhanced results for the Precision, recall and F-measure. 
Keywords: Big Data Analytic, Genetic Algorithm, FP-Growth, Association Rule, Neural Network, Precision, Recall, F-measure.
Scope of the Article: Classification