Research Article Open Access

Accelerated PSO Swarm Search Feature Selection with SVM for Data Stream Mining Big Data

Abstract

In the modern world there is huge development in the field of networking technology which handles huge data at a time. This data can be structured, semi structured or unstructured. To perform efficient mining of valuable information from such type of data the big data technology is gaining importance nowadays. Data mining application is been used in public and private sectors of industry because of its advantage over conventional networking technology to analyze large real time data. Data mining mainly relies on 3 V’s namely, Volume, Varity and Velocity of processing data. Volume refers to the huge amount of data it collects, Velocity refers to the speed at which it process the data and Variety defines that multi-dimensional data which can be numbers, dates, strings, geospatial data, 3D data, audio files, video files, social files, etc. These data which is stored in big data will be from different source at different rate and of different type; hence it will not be synchronized. This is one of the biggest challenges in working with big data. Second challenge is related to mining the valuable and relevant information from such data adhering to 3rd V i.e. Velocity. Speed is highly important as it is associated with cost of processing. This paper focuses detailed study of accelerated PSO Swarm search feature selection and use of support vector machine.

Himani Patel

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