Fast and efficient non-parametric classification and clustering methods for largr data sets
| dc.contributor.author | Babu, V Suresh | |
| dc.date.accessioned | 2015-09-16T08:49:37Z | |
| dc.date.accessioned | 2023-10-20T04:36:38Z | |
| dc.date.available | 2015-09-16T08:49:37Z | |
| dc.date.available | 2023-10-20T04:36:38Z | |
| dc.date.issued | 2009 | |
| dc.description | Supervisor: P Viswanath | en_US |
| dc.description.abstract | Pattern Classification and clustering are two prominent pattern recognition tasks applied in various domains. Non-parametric methods are those which does not assume any model or distribution from for the data. Hence these methods are more general and can give better results provided the data set is a larger one. Nearest neighbor classifier(NNC) and its variants like k nearest neighbor classifier (k-NNC) are popular non-parametric classifiers. They show good performance and has asymptotic behavior comparable to that of the bayes classifier. When it comes to clustering methods, DBSCAN(Density based spatial clustering of applications with noise) uses density which is found non-parametrically at a point in order to derive density based clusters. DBSCAN can find arbitrary shaped clusters(unlike methods like k-means clustering) along with noisy outliers detection... | en_US |
| dc.identifier.other | ROLL NO.04610105 | |
| dc.identifier.uri | http://172.17.1.107:4000/handle/123456789/189 | |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TH-0774; | |
| dc.subject | COMPUTER SCIENCE AND ENGINEERING | en_US |
| dc.title | Fast and efficient non-parametric classification and clustering methods for largr data sets | en_US |
| dc.type | Thesis | en_US |