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Lin, J., Keogh, E., Lonardi, S. and Chiu, B. (2003) A Symbolic Representation of Time Series with Implications for Streaming Algorithms. Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, 13 June 2003, 2-11.
https://doi.org/10.1145/882082.882086

has been cited by the following article:

• 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.