• jaideep kaur
• Mr. Amit Chhabra
• Dhanpreet Singh Dhingra

Data Mining refers to the discovery of data models in large data sets. It is a process of extracting useful information from the datasets. As with the developments in Web 3.0 and Information and Communication Technology (ICT), the cyber world is flooded with petabytes of unstructured and distributed data. Specifically, data mining is a form of machine learning as most data extraction techniques use machine learning algorithms for obtaining useful data segments. Moreover, with the increase in internet utilization, data mining is indeed a necessity for realizing specific applications. Big data, a term coined for the accumulation of large data like petabytes, Exabytes, and so on, requires advance techniques for efficient data abstraction. From the perspective of software, the traditional mining algorithms are applicable only for small scale data. As a consequence, this paper focuses on a reviewing several state-of-the-art data mining techniques and algorithms that are used to process massive data. In addition to these, various advantages and limitations of different approaches are discussed to provide an overview of pros and cons of each technique for efficient utilization in practical environments.

"State-of-the-Art Tools and Techniques for Big Data Mining: A Survey ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.1-8, May-2019, Available :http://www.jetir.org/papers/JETIR1905001.pdf

"State-of-the-Art Tools and Techniques for Big Data Mining: A Survey ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp1-8, May-2019, Available at : http://www.jetir.org/papers/JETIR1905001.pdf