Name
Abstract | ||
|---|---|---|
Outlier detection is a hot issue in data mining, which has plenty of practical applications. Local Outlier Factor algorithm, as a pioneering work of local outlier detection, has been paid much attention. However, it needs to perform the neighbor search with high time complexity and ignores the local distribution of an object within its neighbor. In this work, a novel local outlier detection method based on grid random walk is proposed, which uses random walk to obtain stationary distribution vector of grid information graph. Some grids with small values of stationary distribution vector will be considered as candidate outliers. The outlier detection is performed only on candidate outliers to improve the running efficiency. Then, considering the local distribution of an object within its neighbor, a new local outlier factor is constructed to estimate the abnormal degree of each object. The experimental results indicate that the proposed algorithm has better performance and lower running time than the others. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11227-022-04459-7 | The Journal of Supercomputing |
Keywords | DocType | Volume |
Outlier detection, Local outlier factor, Random walk, Grid information graph | Journal | 78 |
Issue | ISSN | Citations |
12 | 0920-8542 | 0 |
PageRank | References | Authors |
0.34 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunyan She | 1 | 0 | 0.34 |
| Shaohua Zeng | 2 | 0 | 0.34 |