Credit Card User Frequent Buying Predicton Analysis using Cluster Methods
N. Akshaya1, Sundar Santhoshkumar2, E. Ramaraj3
1N. Akshaya, M. Phil Research Scholar of Computer Science.
2Dr. S. Santhoshkumar, Assistant Professor in Computer Science, Alagappa University, Karaikudi, (Tamil Nadu). India
3Dr. E. Ramaraj, Professor and Head in Computer Science, Alagappa University, Karaikudi, (Tamil Nadu). India.
Manuscript received on 28 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2223-2225 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8145078919/19©BEIESP | DOI: 10.35940/ijitee.I8145.078919
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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: Today the world becomes more digital. The cashless transactions are increased in all sectors. The large amounts of data in digital form are generated every day. The companies need to analyze the existing transactions, to predict the user requirements in the future. The payment during the purchase can be done in different modes by the user. In this work, the credit card transactions are analyzed. There are many data mining techniques are used to predict the frequent sets of items during purchase. Data clustering in one of the familiar and widely used technique to identify a similar set of items in a group or dataset. In this work, the two familiar existing techniques k-means and k-mediods are compared with the same datasets. The results show the best clustering algorithm.
Index Terms: Data Mining, Clustering, K-Means, Kmediods.
Scope of the Article: Data Mining