Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining Deviants in a Time Series Database
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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Outlier (also called deviation or exception) detection is an important function in data mining. In identifying outliers, the deviation-based approach has many advantages and draws much attention. Although a linear algorithm for sequential deviation detection is proposed, it is not stable and always loses many deviation points. In this paper, we present three algorithms on detecting deviations. The first algorithm is time proportional to the square of the dataset length, and the second is time proportional to the square of the number of distinct data values. These two algorithms lead to same result, while the latter is much more efficient than the former. In the third algorithm, a deviation factor is defined to help finding deviation points. Although leading to approximation results, it is the most efficient of the three, especially to large datasets with lots of distinct values.