A Survey on Clustering Techniques for Mining Big Data
Keywords:
Data mining, Big data, Clustering techniques, Big data analytics.Abstract
Clustering technique is mining process in which whole dataset is divided in to meaningful subclasses. It is unsupervised classification of data items (feature vectors (FV) observations, or patterns unsupervised) into teams (cluster). Clustering process is very useful in numerous pattern classification, grouping, exploratory pattern analysis, machine learning, document retrieval, image segmentation and decision making. “Big data” is nothing but the datasets whose size in on many facets the power of typical software package data tools to efficiently capture, store, manage and analyze. That is we tend to outline massive information in terms of being bigger than meticulous verity of thousands of gigabyte (terabyte). Big data is useful for populace and corporations but in some cases it is difficult to store and it is also time consuming. So, one of the ways to overcome this problem is to improve the clustering methods, however it suffers from high computational complexity. Data mining is the technique in which helpful information and hidden relationship among data is extracted, but the traditional data mining approaches cannot be directly used for big data due to their inherent complexity. Key objective is to introduce a simple general overview of data clustering categorizations for big data. And also explain and summarizing some of the related work for it. This paper presents a theoretical overview of some of current clustering techniques used for analyzing big data.
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