Detecting and searching system for event on internet blog data using cluster mining Algorithm

Abstract

The popularity of Internet is growing every day with an exponential growth in the information that is being published over it. Apart from static content, dynamic content on the Web is also growing at an increasing rate thanks to blogs, news forums and the likes. Users of such blogs and forums write about their personal life, professional life and events happening in real world such as a cricket match, elections, a product release or disasters. The number of blog entries published on an event is proportional to its popularity. Using this as the basis, we designed a system called EventDS (Event Detection and Searching) which detects major events by analyzing blogs using a novel clustering algorithm called PDDPHAC. We also propose a new representation for events: each event is represented as a Topic Tree where sub-topics are treated as children of their super-topics. © 2012 Springer-Verlag GmbH Berlin Heidelberg.