A new weighted based frequent and infrequent pattern mining method on real-time E-commerce

A new weighted based frequent and infrequent pattern mining method on real-time E-commerce

Thulasi Bikku

Vignan’s Nirula Institute of Technology & Science for Women, Peda Palakaluru, Guntur 522009, Andhra Pradesh, India

Corresponding Author Email: 
31 October 2018
| Citation



The purpose of this research is to perform infrequent pattern mining on E-Commerece Data. Association mining is an interesting model of data mining which is responsible for retrieving correlations, frequent patterns and infrequent associations from la rge datasets. The main objective of infrequent pattern mining is to discover the top infrequent items from the positive and negative association patterns with minimum support and confidence measures. Generally, association rule mining process is processed in 2 phases. Initially itemsets having high threshold values are identified and then secondly generates association patterns from these frequent candidate sets. Association rules can be represented in two forms, one is positive association rules and the other is negative association rules. In this proposed approach, user recommended frequent and infrequent mining model was developed to discover the top frequent and infrequent relational patterns on the e-commerce dataset. User selects his feature product to generate frequent and infrequent association patterns. Based on the feature product, all related candidate sets are generated to the user selected feature product P. These candidate sets are used to discover the frequent and infrequent associations with other feature products. Here weighted infrequent ranking measure was used to filter the infrequent product from the frequent associations. Experimental results show that proposed model has high computational prediction compared to traditional infrequent mining models.


market data, infrequent association rules, support

1. Introduction
2. Related works
3. Top ‘k’ infrequent mining algorithm using ranking measurers
4. Experimental results
5. Conclusion

Agrawal J., Agrawal S., Singhai A., Sharma S. (2015). SET-PSO- based approach for mining positive and negative association rules. Knowledge and Information Systems, pp. 453-471. https://doi.org/10.2141/kis.32141

Amiri E. O. (2018). Application of computational experiments based on the response surface methodology for studying of the recirculation zone in the Y-shaped channel. Mathematical Modelling of Engineering Problems, Vol. 5, No. 3, pp. 243-248. https://doi.org/10.18280/mmep.050317

Arvind T. S., Badhe V. (2015). Discovery of certain association rules from an uncertain database. International Conference on Computational Intelligence and Communication Networks, pp. 827-831. https://doi.org/10.1109/CICN.2015.168

Cavique L. (2007). A scalable algorithm for the market basket analysis. Journal of Retailing and Consumer Services, Vol. 14, No. 6, pp. 400-407. https://doi.org/10.1016/j.jretconser.2007.02.003

Duggirala R., Narayana P. (2013). Mining positive and negative association rules using coherent approach. International Journal of Computer Trends and Technology, Vol. 4, No. 1, pp. 1-8.

Lee D., Park S., Moon S. (2013). Utility-based association rule mining: A marketing solution for cross-selling. Expert Systems with Applications, pp. 2715-2725. https://doi.org/10.2741/esa.22147

Lin K., Liao I., Chen Z. (2011). An improved frequent pattern growth method for mining association rules. Expert Systems with Applications, pp. 5154-5161. https://doi.org/10.2741/esa.11974

Narmadha D., NaveenSundar G., Geetha S. (2011). A novel approach to prune mined association rules in large databases. 2011 3rd International Conference on Electronics Computer Technology, Vol. 5. https://doi.org/10.1109/ICECTECH.2011.5942031

Ouyang W. (2014). Mining positive and negative association rules in data streams with a sliding window. 4th Global Congress on Intelligent Systems, pp. 205-209. https://doi.org/10.1109/GCIS.2013.39

Rodríguez-González A. Y., Martínez-Trinidad J. F., Carrasco- Ochoa J. A., Ruiz-Shulcloper J. (2013). Mining frequent patterns and association rules using similarities. Expert Systems with Applications, pp. 6823-6836.

Said A. M., Dominic P. D. D. (2012). A new scheme for extracting association rules: Market basket analysis case study. International Journal of Business Innovation and Research, Vol. 6, No. 1, pp. 28-46. https://doi.org/10.2214/ijbir.21421.

Setiabudi D. H., Budhi G. S., Purnama I. W., Noertjahyana A. (2011). Data mining market basket analysis’ using hybrid-dimension association rules, case study in minimarket X. International Conference on Uncertainty Reasoning and Knowledge Engineering, pp. 196-199. https://doi.org/10.1109/URKE.2011.6007796

Trnka A. (2010). Market basket analysis with data mining methods. International Conference on Networking and Information Technology, pp. 446-450. https://doi.org/10.1109/ICNIT.2010.5508476

Waghmare V., Mukhopadhyay D. (2014). Mobile Agent based market basket analysis on cloud. International Conference on Information Technology, pp. 305-310. https://doi.org/10.1109/ICIT.2014.21

Yang T., Qian K., Lo D. C., Xie Y., Shi Y., Tao L. (2016). Improve the prediction accuracy of naive bays classifier with association rule mining. IEEE 2nd International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, IEEE International Conference on Intelligent Data and Security, pp. 129-133. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2016.38