Outlier Detection of Transaction Data Using DBSCAN Algorithm
DOI:
https://doi.org/10.61841/fvzwt261Keywords:
Data Mining, Outlier Detection, Euclidean Distance, Clustering, DBSCANAbstract
The supermarket is one means of marketing the company's products. Marketing activities undertaken with supermarkets provide a wide range of types of products from different companies (as producers). Consumers prefer to go to the supermarket than traditional markets due to promotions. For example, the products offered were given a discounted half price of the normal price. Consumers tend to buy more of their needs so that existing stock items in the supermarket can be drastically reduced. Therefore, the supermarket had to anticipate in order to not have a shortage of stock in the warehouse. Various techniques in data mining can be used, one of which is outlier detection. The role of an outlier detection is needed in order to detect abnormal transactions, including candidate anomalies and normal transactions, and will help the supermarket in anticipation of running out of stock items. Outlier detection is an outlier search process on a dataset and is one of the first steps to be able to perform analysis of data coherently. The main objective in outlier detection is to detect data with properties/state data with different data, or are most of the anomalies found in multidimensional datasets. One of the formidable algorithms for detecting outliers is DBSCAN. Therefore, in this study, the author will use the technique of outlier detection algorithm with expected DBSCAN to help supermarkets in anticipation of running out of stock items. The result from research that has been done by calculating 1862 products is that there was no product data that was classified as an outlier, whereas by calculating 100 first products, there are 4 product data that were classified as outliers, products with ids 80069449, 80015728, 82024920, and 80021527.
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