Name
Abstract | ||
|---|---|---|
A Gravity-based Outliers Detection Algorithm GODA is presented Considering that for a data point, both of the density around it and the distance between it and others can influence the outlier's definition, the algorithm can detect the crytic outliers in the dataset. This paper proposes the definitions and tecliniques firstly and then introducts the algorithm detailedly. Experiments have been carried out with real data, and the results indicates that not only the new algorithm is of goog extensible ability but also it has higher efficiency of detecting outliers. It points out the outlier's outlying degree in the dataset as well © 2008 IEEE. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IIH-MSP.2008.48 | Proceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008 |
Keywords | DocType | Volume |
higher efficiency,algorithm detailedly,crytic outliers,outlying degree,gravity,datasets,crytic outlier,data point,goog extensible ability,techniques firstly,object detection,data mining,gravity-based outliers detection algorithm,outlier,new algorithm,outlier detection,gravity-based outliers detection,artificial neural networks,stress,euclidean distance,algorithm design and analysis | Conference | null |
Issue | ISSN | ISBN |
null | null | 978-0-7695-3278-3 |
Citations | PageRank | References |
1 | 0.48 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng JianLiang | 1 | 1 | 0.48 |
| Cheng Weixiang | 2 | 1 | 0.48 |