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An Insight into State-of-the-Art Techniques for Big Data Classification

An Insight into State-of-the-Art Techniques for Big Data Classification

Neha Bansal, R.K. Singh, Arun Sharma
Copyright: © 2017 |Volume: 8 |Issue: 3 |Pages: 19
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522513797|DOI: 10.4018/IJISMD.2017070102
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MLA

Bansal, Neha, et al. "An Insight into State-of-the-Art Techniques for Big Data Classification." IJISMD vol.8, no.3 2017: pp.24-42. http://doi.org/10.4018/IJISMD.2017070102

APA

Bansal, N., Singh, R., & Sharma, A. (2017). An Insight into State-of-the-Art Techniques for Big Data Classification. International Journal of Information System Modeling and Design (IJISMD), 8(3), 24-42. http://doi.org/10.4018/IJISMD.2017070102

Chicago

Bansal, Neha, R.K. Singh, and Arun Sharma. "An Insight into State-of-the-Art Techniques for Big Data Classification," International Journal of Information System Modeling and Design (IJISMD) 8, no.3: 24-42. http://doi.org/10.4018/IJISMD.2017070102

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Abstract

This article describes how classification algorithms have emerged as strong meta-learning techniques to accurately and efficiently analyze the masses of data generated from the widespread use of internet and other sources. In particular, there is need of some mechanism which classifies unstructured data into some organized form. Classification techniques over big transactional database may provide required data to the users from large datasets in a more simplified way. With the intention of organizing and clearly representing the current state of classification algorithms for big data, present paper discusses various concepts and algorithms, and also an exhaustive review of existing classification algorithms over big data classification frameworks and other novel frameworks. The paper provides a comprehensive comparison, both from a theoretical as well as an empirical perspective. The effectiveness of the candidate classification algorithms is measured through a number of performance metrics such as implementation technique, data source validation, and scalability etc.

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