International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 06 Issue: 04 | Apr 2019
p-ISSN: 2395-0072
www.irjet.net
Comparative Analysis of Various Tools for Data Mining and Big Data Mining Mrs. G. SangeethaLakshmi1, Ms. M. Jayashree2 1Asst
Prof,Department of Computer science and Application, DKM College for Women (Autonomous), Vellore. scholar, Department of Computer Science, DKM College for Women (Autonomous), Vellore, TamilNadu. ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract - Data mining and knowledge discovery has Classification – is the task of generalizing known emerged to extract useful, interesting, and unknown structure to apply to new data. For example, an e-mail patterns and knowledge from huge amount of program might attempt to classify an e-mail as database. Big data is the term used to delineate "legitimate" or as "spam". massive amounts of information of both structured and unstructured data types. Data mining techniques can Regression – attempts to find a function which models be classified as classification, association, clustering, the data with the least error that is, for estimating the anomaly detection, regression analysis, prediction, and relationships among data or datasets. tracking patterns. Data mining tools which are helpful to achieve above data mining techniques. This research Summarization – providing a more compact analysis various datamining and big data mining tools representation of the data set, including visualization with different perspectives.This research will help for and report generation. researchers to select appropriate datamining tool or tools for their research. The rapid and inevitable development of technology is causing a substantial global increase in the volume of Keywords—Big data; association; clustering; data. Such data mean better information, and anomalyDetection. information is wealth. This is because information makes it possible for mankind to have a safer and 1. INTRODUCTION: better future, which is the primary goal of scientists Data mining involves six common classes: and researchers. Due to this incredible amount of information that can be obtained from Big Data, Anomaly detection (outlier/change/deviation humanity is able to make considerable progress in detection) – The identification of unusual data records, diverse fields ranging from health and safety to that might be interesting or data errors that require education and economy. further investigation. 2Research
The analysis and modeling of big data are not new subjects for actuaries, bankers, and insurers; DM helps them overcome many difficulties in their aim to manage money more effectively, control the system, reduce or transfer potential risks, understand client requirements, improve funds management, increase market share, and reduce or transfer potential risks . Specifically, DM can be used in the banking and insurance industries to determine default risks and risk groups, specify the correct insurance options for individual customers, increase customer satisfaction, and identify credit card fraud.
Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
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