A Survey of Classification Techniques in the Area of Big Data

Autor:	Koturwar, Praful, Girase, Sheetal, Mukhopadhyay, Debajyoti
Rok vydání:	2015
Předmět:	Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources. Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence. This is a tedious job for users unstructured data. So, there should be some mechanism which classify unstructured data into organized form which helps user to easily access required data. Classification techniques over big transactional database provide required data to the users from large datasets more simple way. There are two main classification techniques, supervised and unsupervised. In this paper we focused on to study of different supervised classification techniques. Further this paper shows a advantages and limitations. Comment: 7 pages, 3 figures, 2 tables in IJAFRC, Vol.1, Issue 11, November 2014, ISSN: 2348-4853
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1503.07477 Zobrazit plný text záznamu View this record from Arxiv