Review on: Techniques for Predicting Frequent Items


EOI: 10.11242/viva-tech.01.01.05

Download Full Text here



Citation

Himanshu A. Chaudhari, Darshana S. Vartak, Nidhi U. Tripathi, Sunita Naik, "Review on: Techniques for Predicting Frequent Items", VIVA-Tech IJRI Volume 1, Issue 1, Article 5, pp. 1-8, Oct 2018. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

Electronic commerce(E- Commerce) is the trading or facilitation of trading in products or services using computer networks, such as the Internet. It comes under a part of Data Mining which takes large amount of data and extracts them. The paper uses the information about the techniques and methods used in the shopping cart for prediction of product that the customer wants to buy or will buy and shows the relevant products according to the cost of the product. The paper also summarizes the descriptive methods with examples. For predicting the frequent pattern of itemset, many prediction algorithms, rule mining techniques and various methods have already been designed for use of retail market. This paper examines literature analysis on several techniques for mining frequent itemsets.The survey comprises various tree formations like Partial tree, IT tree and algorithms with its advantages and its limitations.

Keywords

Association Rule Mining, Data Mining, Frequent Itemsets, IT tree, Market Basket Data, Prediction.

References

  1. K. Wickramaratna and M. Kubat, “Predicting Missing Item In Shopping Cart”, IEEE Transactions On Knowledge And Data Engineering, Volume 21 Issue 7, July 2009.
  2. F. Coenen, P. Leng, and S. Ahmed, “Data Structure for Association Rule Mining: T-Trees and P-Trees”, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 6, June 2004.
  3. M. Kubat, A. Hafez, V. V. Raghavan, J. Lekkala, And W. K. Chen, “Itemset Trees For Targeted Association Querying”, IEEE Transactions On Knowledge And Data Engineering, Vol. 15, No. 6, November/December 2003.
  4. C. Aggarwal, C. Procopiuc and P. Yu, “Finding Localized Associations In Market Basket Data”, IEEE Transactions On Knowledge And Data Engineering, Vol. 14, No. 1, January/February 2002.
  5. P. Meshram, D. Gupta, P. Dahiwale, “An Approach For Predicting The Missing Items From Large Transaction Database”, IEEE Sponsored 2nd International Conference On Innovations In Information Embedded And Communication Systems Iciiecs’15.
  6. S. Yende, P. Shirbhate, “Review On: Prediction Of Missing Item Set In Shopping Cart”, International Journal Of Research In Science & Engineering, Volume 1, Issue 1, April 2017.
  7. R. Bodakhe, P. Gotarkar, A. Dahiwade, P. Gosavi, J.Syed, “A Sequential Approach For Predicting Missing Items In Shopping Cart Using Apriori Algorithm”, Imperial Journal Of Interdisciplinary Research (IJIR) Volume 3, Issue4, 2017.
  8. J. Vohra, “Data Mining Approach For Retail Knowledge Discovery”, International Journal Of Advanced Research In Computer Science And Software Engineering, Volume 6, Issue 3, March 2016.
  9. J. Heaton, “Comparing Dataset Characteristics That Favour the Apriori, Eclat or FP-Growth Frequent Itemset Mining Algorithms”, 30 Jan 2017.
  10. M. Nirmala, V. Palanisamy, “An Enhanced Prediction Technique For Missing Itemset In Shopping Cart”, International Journal Of Emerging Technology And Advanced Engineering, Volume 3, Issue 7, July 2013.
  11. K. Kumar, S. Sairam, “Predicting Missing Items In Shopping Cart Using Associative Classification Mining”, International Journal Of Computer Science And Mobile Computing, Volume 2, Issue 11, November 2013.
  12. H. Deulkar, R. Shelke, “Implementation of Users Approach for Item Prediction and Its Recommendation In Ecommerce”, International Journal Of Innovative Research In Computer And Communication Engineering, Volume 5, Issue 4, April 2017.
  13. S. Neelima, N. Satyanarayana and P. Krishna Murthy, “A Survey On Approaches For Mining Frequent Itemsets”, IOSR Journal Of Computer Engineering (IOSR-JCE), Volume 16, Issue 4, Ver. Vii, (Jul – Aug. 2014), Pp 31-34.
  14. M. Ingle, N. Suryavanshi, “Association Rule Mining Using Improved Apriori Algorithm”, International Journal Of Computer Applications, Volume 112,Issue 4, February 2015.
  15. M. Nirmala. and V. Palanisamy, “An Efficient Prediction Of Missing Itemset In Shopping Cart”, Journal Of Computer Science, Volume 9 (1), 2013, pp 55-62.