An Improved DBSCAN Algorithm for High Dimensional Datasets

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.

Autorentext

Glory H. Shah completed her Master of Technology in computer Engineering from Dharmsinh Desai University, Nadiad, Gujarat, India. She is also an Assistant Professor at Marwadi Education foundation group of Institute Rajkot, Gujarat, India. Her current research interest includes Data Mining Clustering (Density-Based Clustering), Distributed Database.