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A 8-1 Top-k Distance-based Outlier Detection on Uncertain Dataset
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shaikh, Salman Ahmed Kitagawa, Hiroyuki |
| Copyright Year | 2013 |
| Abstract | This paper studies the problem of top-k distance-based outlier detection on uncertain data. In this work, an uncertain object is modelled by a Gaussian probability density function. Since the Naive approach is very expensive due to costly distance function between uncertain objects, a populated-cell list (PC-list) based top-k distance-based outlier detection approach is proposed in this work. Where PC-list is a sorted list of non-empty cells of a grid (grid is used to index dataset objects). Using PC-list, the top-k outlier detection algorithm needs to consider only a fraction of dataset objects and hence quickly identifies candidate objects for top-k outliers. An extensive empirical study shows that our proposed approach is effective, efficient and scalable. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://db-event.jpn.org/deim2013//proceedings/pdf/a8-1.pdf |
| Alternate Webpage(s) | http://www.kde.cs.tsukuba.ac.jp/~salman/files/paperPDFs/DEIM2013.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
