Clustering and Pattern Mining of Customer Transaction Data using Apriori Algorithm
Sonali L. Mortale1, Manisha J. Darak2
1Sonali L. Mortale, Department of Computer Engineering, Siddhant College of Engineering Sadumbre, Pune, India.
2Prof. Manisha J. Darak, Department of Computer Engineering, Siddhant College of Engineering Sadumbre, Pune, India.
Manuscript received on 20August 2019. | Revised Manuscript received on 26 August 2019. | Manuscript published on 30 September 2019. | PP: 8035-8040 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6424098319/2019©BEIESP | DOI: 10.35940/ijrte.C6424.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering customer transaction data is an important procedure for analyzing customer behavior in retail and e-Commerce. Clustering of trading data with finding patterns using Apriori algorithm will helps to develop a market strategy and increases the profit. The system uses Apriori algorithm for finding pattern. The input of Apriori algorithm is the output of Customer Transaction Clustering Algorithm. In a system the customer transaction data is presented by using transaction tree and the distance between them is also calculated. Cluster the customer transaction data by using customer transaction clustering algorithm. The system selects frequent customer as representatives of customer groups. Finally, the system forwards the output of clustering to Apriori algorithm for finding patterns.
Keywords: Clustering, Apriori Algorithm, Customer Transaction Clustering Algorithm, Transaction Tree.
Scope of the Article: Clustering