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IJSTR >> Volume 8 - Issue 10, October 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Target Projection Pursuit Feature Selection Quadratic Associative Classifier for Time Series Big Data Prediction

[Full Text]



A.Selvakumar, Dr.S.Prasath



Time series big data, feature selection, Targeted Interactive Projection Pursuit, Jaccard similarity, Quadratic Associative classifier, association rules, space complexity.



Big data is a collection of data that are large in size and growing exponentially with respect to time. A time series is a sequence of monitored data over time. The various methods have been developed in the time series analysis. But the accurate prediction was not performed with minimum time. In order to improve the prediction accuracy with minimum time, an efficient Targeted Interactive Projection Pursuit Feature Selection based Quadratic Associative Data Classification (TIPPFS-QADC) technique is introduced. Initially, the TIPPFS-QADC technique collects a large volume of data from the big dataset. The TIPPFS-QADC technique comprises the two processes namely feature selection and classification. The TIPPFS-QADC technique uses Targeted Interactive Projection Pursuit for performing the feature selection in a time series database for reducing the prediction time and space complexity. Targeted Projection Pursuit is a statistical technique used to explore the space of projections through manipulating the Jaccard similarity between the features. After performing the feature selection, Quadratic Associative Data Classification is carried out to predict the future results of time series data. Quadratic Associative Classifier (QAC) is a supervised learning model that uses association rules to separate the two or more classes. Through performing the classification, the time series data prediction is carried out with superior accuracy and lesser time consumption. Experimental evaluation of proposed TIPPFS-QADC technique and existing methods are carried out using big dataset. The results observation clearly shows that the proposed TIPPFS-QADC technique obtains higher prediction accuracy and minimum false positive rate, prediction time and space complexity.



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