The work flow model can be implemented with the data mining in the E-commerce platforms. It helps the product/project manager in several ways. The multiple queries have figured out and those are solved here. The data and reviews are generated automatically. The text are generated with web crawler and stored in database as a raw data. The data are cleaned with Natural Language Processing methods and algorithms. The specific types of algorithms are digitally defined for this framework. The specific type of algorithm is run for specific new cases in different platforms. This is designed in a manner to be used by the humans for the interaction purpose. Python is used for pulling data out of files. The process gets automated and the data is cleaned to attain the efficiency. The data review has taken and classified with the data cleaning process with Natural Language Processing. The Natural Language Processing techniques are used, they are Sentimental Analysis, Topic Modeling, Text Generation.
Cite this article:
Madhumathi S, Gomathi R. Data mining in Ecommerce platforms for product managers. Research J. Engineering and Tech. 2021;12(1):01-07. doi: 10.5958/2321-581X.2021.00001.5
Madhumathi S, Gomathi R. Data mining in Ecommerce platforms for product managers. Research J. Engineering and Tech. 2021;12(1):01-07. doi: 10.5958/2321-581X.2021.00001.5 Available on: https://ijersonline.org/AbstractView.aspx?PID=2021-12-1-1
1. Hossein Niavand, Farzaneh Haghighat Ni, Data mining, Applications Tools in Insurance Strategies, 10.5958/2321-5763.2020.00007.4 Journal: Asian Journal of Management
2. Ashish Tamrakar, Deepty Dubey, Query Optimization using Natural Language Processing, International Journal of Technology
3. Gelivi Harish, J. Andrews, Effective Implementation of Data Segregation and Extraction Using Big Data in E-Health Insurance as a Service, DOI: 10.5958/2321-581X.2015.00037.9, Journal: Research Journal of Engineering and Technology
4. Akash Kataria, Venkatesh P, Smarter Cities Data Management – A Comparative Analysis in Big Data, DOI: 10.5958/2321-581X.2019.00006.0, Journal: Research Journal of Engineering and Technology
5. P. Shanmuga Sundari, M. Subaji, J. Karthikeyan, A Survey on effective similarity Search Models and Techniques for Big data Processing in Healthcare System, DOI: 10.5958/0974-360X.2017.00476.0, Journal: Research Journal of Pharmacy and Technology
6. Leo DencelinX, Ramkumar T, Distributed Machine Learning Algorithms to classify Protein secondary structures for Drug Design – A Survey, DOI: 10.5958/0974-360X.2017.00564.9, Journal: Research Journal of Pharmacy and Technology
7. Padmavathi Vanka, T. Sudha, Big Data Technologies: A Case Study, DOI: 10.5958/2349-2988.2017.00109.7, Journal: Research Journal of Science and Technology
8. S. Kavitha, T. Sabhanayagham, R. Thenmozhi, Analysis of Body Mass Index Based on Correlation and Regression, DOI: 10.5958/0974-360X.2018.00415.8, Journal: Research Journal of Pharmacy and Technology
9. Vimal Kumar Stephen. K, V. Mathivanan, Adjusting Healthcare Innovation and Software Necessities through design thinking, DOI: 10.5958/0974-360X.2017.00639.4, Journal: Research Journal of Pharmacy and Technology
10. Sornalakshmi. K, Vadivu. G, Sujatha G, Hemavathi. D, A Survey on using Social Media Data Analytics for Pharmacovigilance, DOI: 10.5958/0974-360X.2017.00621.7, Journal: Research Journal of Pharmacy and Technology
11. Kumaran U*, Neelu Khare, A Sai Suraj, Privacy Preserving in Data Mining Technical: A Review, Research J. Pharm. and Tech 2016; 9(11): 2074-2076.
12. V. Mareeswari*, Saranya R, Mahalakshmi R, Preethi E, Prediction of Diabetes Using Data Mining Techniques, Research J. Pharm. and Tech. 2017; 10(4): 1098-1104.DOI: 10.5958/0974-360X.2017.00199.8
13. Uma M, Sneha V, Sneha G, Bhuvana J and Bharathi B,” Formation of SQL from Natural Language Query using NLP,” in IEEE, 2019 International Conference on Computational Intelligence in Data Science (ICCIDS).
14. C. Hitoshi ISAHARA,” Resource-based Natural Language Processing,” National Institute of Information and Communications Technology 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan, in IEEE, 2007 International Conference on Natural Language Processing and Knowledge Engineering.
15. Yinglong Diao, Ke-yan Liu, Xiaoli Meng, Xueshun Ye, Kaiyuan He, ”A Big Data Online Cleaning Algorithm Based on Dynamic Outlier Detection,” IEEE, 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.
16. Virender Kumar, Cherry Khosla, J. Lee, P. Lee, S. Lee, A. Yuille and C. Koch, “Data Cleaning – A thorough analysis and survey on Unstructured data”, IEEE, 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Conﬂuence)
17. Ruslan Posevkin, Igor Bessmertny,” Translation of natural language queries to structured data sources,” IEEE, 2015 9th International Conference on Application of Information and Communication Technologies (AICT)