Name
Abstract | ||
|---|---|---|
Outliers in the database are the objects that deviate from the rest of the dataset by some measure. The Nearest Neighbor Outlier Factor is considering to measure the degree of outlier-ness of the object in the dataset. Unlike the other methods like Local Outlier Factor, this approach shows the interest of a point from both neighbors and reverse neighbors, and after that, an object comes into consideration. We have observed that in GBBK algorithm that based on K-NN, used quick sort to find k nearest neighbors that take O N log N time. However, in proposed method, the time required for searching on K times which complete in O KN time to find k nearest neighbors k < < log N. As a result, the proposed method improves the time complexity. The NSL-KDD and Fisher iris dataset is used, and experimental results compared with the GBBK method. The result is same in both the methods, but the proposed method takes less time for computation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJKDB.2017010105 | IJKDB |
Field | DocType | Volume |
k-nearest neighbors algorithm,Local outlier factor,Anomaly detection,Pattern recognition,Computer science,sort,Outlier,Artificial intelligence,Iris flower data set,Time complexity,Intrusion detection system,Machine learning | Journal | 7 |
Issue | ISSN | Citations |
1 | 1947-9115 | 1 |
PageRank | References | Authors |
0.43 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santosh Kumar Sahu | 1 | 1 | 0.77 |
| Sanjay Kumar Jena | 2 | 101 | 14.37 |
| Manish Verma | 3 | 1 | 0.43 |
| SahuSantosh Kumar | 4 | 1 | 0.43 |
| JenaSanjay Kumar | 5 | 27 | 1.65 |