Social Networking Data Research Using Frequent Pattern Mining and Machine Learning Data
Lakshmi N1, Krishnamurthy M2
1Lakshmi N*, Research Scholar, CSE, KCG College of Technology, Chennai, (Tamil Nadu), India.
2Krishnamurthy M, Professor, CSE, KCG College of Technology, Chennai, (Tamil Nadu), India.
Manuscript received on February 01, 2019. | Revised Manuscript received on February 14, 2019. | Manuscript published on August 30, 2019. | PP: 4784-4789 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9352088619/2019©BEIESP | DOI: 10.35940/ijeat.F9352.088619
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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 data are generated by the sources are very large in number with variety of form. These data are organized in to specific format in order to handle properly. Data mining methods are addressed various problem during data extraction process to analytical process. The relevant data are extracted by applying pattern over the huge databases. Association rule mining introduces the method to extracts the related data from the datasets using the performance metrics like support and confidence. Traditional algorithm uses this metrics which is restricted to common attribute format. This problem is addressed by using generic attribute format with frequent pattern mining. The main objective of the paper is to analyze the algorithm and performance metrics related to the frequent patter mining or relevant data. Association rule mining has analyzed with various parameters in single connectivity and multi connectivity rules. Social networking suffers various problem because of uncertain data arrived for processing which is analyzed with various efficiency related elements. The analysis and prediction are also compared with the machine algorithms like classification and clustering and so on. Various frequent pattern mining algorithm is analyzed and review has been carried out based on the performance level.
Keywords: Data Mining, Frequent Pattern Mining, Association Rule Mining, Machine Learning, Social Networking.