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Predicting the Buying Behaviour Pattern of Grocery Items by Women Consumers: An Empirical Study of Thanjavur


Affiliations
1 Associate Dean, IBS, Mumbai, India
     

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Apriori algorithm is one of the major algorithm used in mining to find frequent item sets in a large transactional data. Companies ranging from small scale to large scale all data is stored in various forms which keeps on growing. If the data is distributed all over the places in a vertically fragmented way then it is very difficult to combine the data and store it in a central location. From the data base entry we can understand that some items are repeatedly entered and they are having the common associations between them.

From this study we can understand that apriori algorithm is the best algorithm and it takes less execution time and it gives the strong association rules. By this association rule we can get the relationships between one item and several important attributes. From the visualization tools to show the results helping to give decision proposals of the grocery item.


Keywords

Super Market, Data Mining, Association Rule, Buying Behaviour, Apriori and Eclat Algorithm, Pattern.
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  • Ashish Goel, Bhawana Mallick,, Customer Purchasing Behavior using Sequential Pattern Mining Technique, International Journal of Computer Applications (0975–8887) Volume 119 – No.1, June 2015, pno 24-31.

  • Mahesh A. Shinde, K. P. Adhiya (2016), Frequent Itemset Mining Algorithms for Big Data using MapReduce Technique - A Review, International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016), ISSN: 2231-5381, p.no 473-477.

  • Mark Patron (2004), Case Study: Applying RFM segmentation to the SilverMinds catalogue, https://link.springer.com/article/10.1057/palgrave.im.4340243, January 2004, Volume 5, Issue 3, pp 269–275.

  • Rana Ishita, Amit Rathod, Eclat with Large Data base Parallel Algorithm and improve its efficiency, International Journal of Computer Applications (0975–8887).

  • Siddhrajsinh Solanki, Neha Soni, A Survey on Frequent Pattern Mining Methods Apriori, Eclat, FP growth, International Journal of Computer Techniques -– Volume X Issue X, Year,open access

  • Zhifang LI, , Xiufang LIU, , Xu CAO, A Study on Improved Eclat Data miningAlgorithm, Advanced Materials Research Online:2011-09-02 ISSN: 1662-8985, Vols.328-330, pp 1896-1899. doi:10.4028/www.scientific.net/AMR.328-330.1896 © 2011.Trans Tech Publications, Switzerland.


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  • Predicting the Buying Behaviour Pattern of Grocery Items by Women Consumers: An Empirical Study of Thanjavur

Abstract Views: 170  |  PDF Views: 0

Authors

Kavitha Venkatachari
Associate Dean, IBS, Mumbai, India

Abstract


Apriori algorithm is one of the major algorithm used in mining to find frequent item sets in a large transactional data. Companies ranging from small scale to large scale all data is stored in various forms which keeps on growing. If the data is distributed all over the places in a vertically fragmented way then it is very difficult to combine the data and store it in a central location. From the data base entry we can understand that some items are repeatedly entered and they are having the common associations between them.

From this study we can understand that apriori algorithm is the best algorithm and it takes less execution time and it gives the strong association rules. By this association rule we can get the relationships between one item and several important attributes. From the visualization tools to show the results helping to give decision proposals of the grocery item.


Keywords


Super Market, Data Mining, Association Rule, Buying Behaviour, Apriori and Eclat Algorithm, Pattern.

References