Name
Abstract | ||
|---|---|---|
In this paper, we give an approximate algorithm for distance based outlier detection using Locality Sensitive Hashing (LSH) technique. We propose an algorithm for the centralized case wherein the entire dataset is locally available for processing. However, in case of very large datasets collected from various input sources, often the data is distributed across the network. Accordingly, we show that our algorithm can be effectively extended to a constant round protocol with low communication costs, in a distributed setting with horizontal partitioning. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2063576.2063948 | CIKM |
Keywords | Field | DocType |
outlier detection,locality sensitive hashing,entire dataset,various input source,large datasets,centralized case,low communication cost,constant round protocol,approximate algorithm,horizontal partitioning,data mining | Locality-sensitive hashing,Anomaly detection,Data mining,Pattern recognition,Computer science,Artificial intelligence | Conference |
Citations | PageRank | References |
4 | 0.43 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madhuchand Rushi Pillutla | 1 | 5 | 0.78 |
| Nisarg Raval | 2 | 68 | 5.85 |
| Piyush Bansal | 3 | 28 | 4.44 |
| Kannan Srinathan | 4 | 422 | 41.70 |
| C. V. Jawahar | 5 | 1700 | 148.58 |