The Significance of using Data Extraction Methods for an Effective Big Data Mining Process

Data is identified as the fuel of modern society for its versatility of use and effectiveness of use. In addition, modern businesses are making a decline based on analysis of historical data and patterns of the data. Such dependency on data analysis makes the process of data analysis important for data mining. Therefore the overall study has shed light on the significance of the data mining process and extraction process of data in order to make a data-driven decision. Additionally, the problems related to the process of data extraction and data mining are mentioned in the study which helps to achieve an overall concept for the data extraction and data mining process. Additionally, the significance of the process is mentioned in the study. Additionally, there are tables constructed that represent problems of the data extraction process and mining and the significance of the stems of mining. The study concludes in a way that helps in the implication of data extraction methods for business.

[1]  Özerk Yavuz A Classification and Clustering Approach Using Data Mining Techniques in Analysing Gastrointestinal Tract , 2022, International scientific and vocational studies journal.

[2]  Mazhar Javed Awan,et al.  Review on Effective Disease Prediction through Data Mining Techniques , 2021, International Journal on Electrical Engineering and Informatics.

[3]  Ahmed Hosny Ghazi,et al.  A Proposed Model for Predicting Employee Turnover of Information Technology Specialists Using Data Mining Techniques , 2021, International journal of electrical and computer engineering systems.

[4]  T. Mohammed Chikouche,et al.  A Strategy of Direct Power Control for PWM Rectifier Reducing Ripple in Instantaneous Power , 2021 .

[5]  M. A. Sadeeq,et al.  Comprehensive Survey of Big Data Mining Approaches in Cloud Systems , 2021 .

[6]  Bhasker Pant,et al.  Demographic profile building for cold start in recommender system: A social media fusion approach , 2021 .

[7]  T. Senthil Kumar,et al.  Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm , 2020, September 2020.

[8]  Yongyun Cho,et al.  Seoul bike trip duration prediction using data mining techniques , 2020, IET Intelligent Transport Systems.

[9]  J. J. Williams,et al.  Mining Big Data in Education: Affordances and Challenges , 2020, Review of Research in Education.

[10]  Jun Lyu,et al.  Brief introduction of medical database and data mining technology in big data era , 2020, Journal of evidence-based medicine.

[11]  Cristóbal Romero,et al.  Educational data mining and learning analytics: An updated survey , 2020, WIREs Data Mining Knowl. Discov..

[12]  Erik M. Fredericks,et al.  Uncertainty in big data analytics: survey, opportunities, and challenges , 2019, Journal of Big Data.

[13]  Yi Wang,et al.  Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , 2019, Applied Sciences.

[14]  Iwin Thanakumar Joseph S Dr,et al.  SURVEY OF DATA MINING ALGORITHM’S FOR INTELLIGENT COMPUTING SYSTEM , 2019, Journal of Trends in Computer Science and Smart Technology.

[15]  Saurabh Pal,et al.  Classification of Skin Disease using Ensemble Data Mining Techniques , 2019, Asian Pacific journal of cancer prevention : APJCP.

[16]  Pooya Tabesh,et al.  Implementing big data strategies: A managerial perspective , 2019, Business Horizons.

[17]  Lejla Turulja,et al.  Text Mining for Big Data Analysis in Financial Sector: A Literature Review , 2019, Sustainability.

[18]  R. B. Ahmad,et al.  A dynamic K-means clustering for data mining , 2019, Indonesian Journal of Electrical Engineering and Computer Science.

[19]  Basma Jumaa Saleh,et al.  A REVIEW PAPER: ANALYSIS OF WEKA DATA MINING TECHNIQUES FOR HEART DISEASE PREDICTION SYSTEM , 2020 .

[20]  M. Faizan,et al.  Applications of Clustering Techniques in Data Mining: A Comparative Study , 2020 .