Publication Date: 2020/10/13
Abstract: The objective is to analyze the given data sets V1 and V2 from the bank_authentication_notes.csv which is taken from openML datasets, is to identify the forged and real notes using K-Means Clustering Concept forming two distinct clusters of real and forged notes. Kmeans is easy and simple uses unsupervised learning to solve clustering related problems. It classifies the given datasets to form a group of clusters based on some similarities. The major goal is defining k centers, one for each cluster. The ultimate aim is to use this dataset to train a machine to detect fake notes automatically. However, before implementation, it is important to access if this dataset can sufficiently distinguish forged banknotes from genuine ones. Hence, in this report, with k-mean cluster analysis, unsupervised machine learning, performed on the datasets, we will visualize and outline the results and make according to recommendations.
Keywords: K-Means Clustering, unsupervised Learning, Clusters, banknotes
DOI: No DOI Available
PDF: https://ijirst.demo4.arinfotech.co/https://ijisrt.com/assets/upload/files/IJISRT20OCT060.pdf.pdf
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