Main Article Content
Abstract
ct The fully integrated text mining system is designed to support the complex and highly literaturedependent task of mining datasets assessment. The task is critical because IT Keywords play an important role in everyday life and their potential risk to technical results must be evaluated. With thousands of IT Keywords introduced every year, many countries worldwide have established increasingly strict laws governing their production and use. The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. The user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. The improvement on the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on K-SVM Mining data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance. The project is to prove an RSS resource lies with the client. These statistics on refresh frequency and volatility illustrate the challenge faced by a proxy in satisfying user needs. As the number of users and servers grow, service personalization through targeted data delivery by a proxy can serve as a solution for better managing system resources. In addition, the use of profiles could lower the load on RSS servers by accessing them only to satisfy a user profile.