A Survey of Classification Techniques in the Area of Big Data
This is an incremental survey paper that reviews existing methods for classifying big data, primarily for researchers or practitioners in data management.
The paper surveys supervised classification techniques for handling unstructured big data, aiming to organize it for easier user access, but does not present new experimental results or concrete numbers.
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.