Comparative Analysis of Information Extraction Techniques for Data Mining

Background/Objectives: This paper emphasizes the evolution of data processing adroitness to advanced data processing taxonomy from Mesolithic to recent years and a comparative study of prevailing tools/techniques which are useful for mainly the analysis of the bulky data. Methods/Statistical Analysis: There are various kinds of methods adapted by researchers for analysis of large amount of data. Each method varies on the basis of their different parameters and datasets according to their needs. These methods are implemented on HDFS, Mapreduce and Hadoop environment with integration of R tool. Some Methods are enhanced by the sentimental analysis through NLP which increase the performance of density analysis. Findings: The data or associated facts have been in existence right with the birth of human species. It commenced with manual illustration and gradually advanced through current state-of the art storage and processing. Big data involves novel techniques to manage information within limited run time. Big data is acutely beneficial in ventures growth, society incumbency and scientific research. The paper provides an overview of state of the art and focuses on the usage of conventional tools as well as advanced tools and techniques for effective information extraction. Applications/Improvements: To handle this prodigious data, there is a need to upgrade from the traditional data filtering techniques and adopt the new big data diagnostic tools.