TITLE:
Anomaly Detection of Store Cash Register Data Based on Improved LOF Algorithm
AUTHORS:
Ke Long, Yuhang Wu, Yufeng Gui
KEYWORDS:
Cash Register Data, Anomaly Detection, K-Means Clustering, Optimized LOF Algorithm, SAX Test
JOURNAL NAME:
Applied Mathematics,
Vol.9 No.6,
June
29,
2018
ABSTRACT:
As the cash register system gradually prevailed in shopping malls, detecting
the abnormal status of the cash register system has gradually become a hotspot
issue. This paper analyzes the transaction data of a shopping mall. When
calculating the degree of data difference, the coefficient of variation is used as
the attribute weight; the weighted Euclidean distance is used to calculate the
degree of difference; and k-means clustering is used to classify different time
periods. It applies the LOF algorithm to detect the outlier degree of transaction
data at each time period, sets the initial threshold to detect outliers, deletes
the outliers, and then performs SAX detection on the data set. If it does
not pass the test, then it will gradually expand the outlying domain and repeat
the above process to optimize the outlier threshold to improve the sensitivity
of detection algorithm and reduce false positives.