| Description |
We present algorithms for detecting spatial anomaly in a time efficient manner. There are many other approaches to solve the same problem but they face a serious issue of very huge computational time. We came up with some novel algorithms which help us to solve the problem in a time efficient manner for very large data sets. We tried to show, by executing experiments on both synthetic and real world data set, that the results obtained from the original data set and the sampled data set are very similar and therefore we executed all our approaches on sampled data set rather than on the original data set. Thus we saved a lot of computational time by using sampled data set as an input to our approaches. |
