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An Improved Dbscan Algorithm for High Dimensional Datasets: an Improvement in Terms of Number of Clusters an in General Increasing the Accuracy of Algorithm Glory Shah
An Improved Dbscan Algorithm for High Dimensional Datasets: an Improvement in Terms of Number of Clusters an in General Increasing the Accuracy of Algorithm
Glory Shah
Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods. It is the art of detecting groups of similar objects in large data sets without having specified groups by means of explicit features. The problem of detecting clusters of points is challenging when the clusters are of different size, density and shape. The development of clustering algorithms has received a lot of attention in the last few years and many new clustering algorithms have been proposed. Thus this book provides detailed knowlege regarding density based clustering algorithms and an improvement over one of the algorithm.
| Media | Books Paperback Book (Book with soft cover and glued back) |
| Released | June 16, 2012 |
| ISBN13 | 9783659140259 |
| Publishers | LAP LAMBERT Academic Publishing |
| Pages | 140 |
| Dimensions | 150 × 8 × 226 mm · 213 g |
| Language | English |
See all of Glory Shah ( e.g. Paperback Book )