Name
Abstract | ||
|---|---|---|
In classification of binary featured data, distance computation is carried out by considering each feature. We represent the given binary data as run-length encoded data. This would lead to a compact or compressed representation of data. Further, we propose an algorithm to directly compute the Manhattan distance between two such binary encoded patterns. We show that classification of data in such compressed form would improve the computation time by a factor of 5 on large handwritten data. The scheme is useful in large data clustering and classification which depend on distance measures. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.patcog.2006.05.002 | Pattern Recognition |
Keywords | Field | DocType |
Non-lossy compression,Classification of compressed data,Run-length | Artificial intelligence,Cluster analysis,Computation,Binary number,Distance measurement,Pattern recognition,Lossy compression,Euclidean distance,Algorithm,Binary data,Mathematics,Machine learning,Distance measures | Journal |
Volume | Issue | ISSN |
40 | 1 | 0031-3203 |
Citations | PageRank | References |
3 | 0.51 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Ravindra Babu | 1 | 57 | 6.26 |
| M. Narasimha Murty | 2 | 824 | 86.07 |
| V.K. Agrawal | 3 | 10 | 2.61 |