원문정보
초록
영어
With the explosive growth of the data stored in various forms, need for innovative and effective technologies to find and use information and knowledge from a large variety of data sources which is continually increasing. Web information contains a lot of noise. Web Mining is the application of data mining techniques to discover classification of web data. It focuses on techniques that could predict the data’s class while the user interacts with the web. The aim of this paper is to measure, propose and improve the use of advance web page classification techniques which is highly used in the advent of mining large web pages based data sets which allows data analysts to conduct more efficient execution of large scale web pages data searches. Thus in this paper researchers introduce an improved concept which may reduce the search space using classification techniques with optimization technique.
목차
1. Introduction
2. Data Mining Techniques
3. Web Data Mining and Web Classification
3.1. Web Structure Mining
3.2. Web Content Mining
3.3. Web Usage Mining
3.4. Web Page Classification Mining
3.5. Web Page Classification Techniques
4. Optimization Techniques
4.1. Firefly Algorithm
4.2. Cuckoo Search
4.3. Bat Algorithm
4.4. ABC Algorithm
4.5. PSO Algorithm
4.6. Intelligent Water Drops (IWDs)
4.7. Ant colony Optimization (ACO)
5. Comparison of Different Optimization Techniques
6. Support Vector Machine
6.1. Linear SVC
6.2. Non –linear SVC
6.3. Non Separable Case
7. Role of Support Vector Machine (SVM) in Web Classification Optimized by Firefly
8. Conclusions and Future Work
References