Automatic Web Page Classification by Combining Feature Selection Techniques and Lazy Learners

The analysis of large datasets has become an important tool in understanding complex systems in areas such as economics, business, science and engineering. Such datasets are often collected geographically distributed way and cannot in practice be gathered in to a single repository. Applications that work with such datasets cannot control most aspects of the data's partitioning and arrangements. So far, attention in data mining process has always focused on extracting information from data physically located at one central site and they often do not consider the resource constraints of distributed and mobile environments. Few attempts were also made in parallel data mining. However most real life applications rely on data distributed in several locations. As a consequence both new architectures and new algorithms are needed. In this paper author proposes a method that explores the capabilities of mobile agents to build an appropriate frame work and an algorithm that better suits the distributed data mining applications. it also makes the performance analysis and comparison with the existing such method.