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@article{144414,
        author = {Shweta},
        title = {Clustering based credit card fraud detection system using validation method with modified K-mean},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {11},
        pages = {99-102},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144414},
        abstract = {As the credit card has become the most prevailing mode of payment for both offline as well as online mode of transaction. This leads to acceleration of frauds. Fraud is a million dollar business which is rising rapidly. Thus there is a need of fraud avoidance system. Fraudster many a time try to generate credit card numbers by just altering the digits. Clustering can be done to effectively implement such systems. The K-mean is one of the most popular clustering algorithm used in data mining for real world applications, especially for the numerical dataset. K-mean performance and efficiency is greatly affected by the initial cluster centers, as different initial centers may leads to different cluster formations. In this research paper, K-mean is used in application with a modification which has an additional step for selecting better cluster centroids. This is done by keeping intercluster similarity as low while intracluster similarity as high.},
        keywords = {credit card fraud, centroids, clustering,k-mean},
        month = {},
        }