Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic
[ACM Press the ninth ACM SIGKDD international conference - Washington, D.C. (2003.08.24-2003.08.27)] Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03 - Mining distance-based outliers in near linear time with randomization and a simple pruning rule
โ Scribed by Bay, Stephen D.; Schwabacher, Mark
- Book ID
- 120625941
- Publisher
- ACM Press
- Year
- 2003
- Weight
- 216 KB
- Category
- Article
- ISBN-13
- 9781581137378
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
This Conference Brings Together Researchers And Practitioners And Focuses On New Developments In Knowledge Discovery And Data Mining. The Challenge Of Extracting Knowledge From Data Is An Area Of Common Interest To Researchers In Several Fields, Including Statistics, Databases, Pattern Recognition,
This Conference Brings Together Researchers And Practitioners And Focuses On New Developments In Knowledge Discovery And Data Mining. The Challenge Of Extracting Knowledge From Data Is An Area Of Common Interest To Researchers In Several Fields, Including Statistics, Databases, Pattern Recognition,