원문정보
초록
영어
A plethora of big data applications are emerging and being researched in the computer science community which require online classification and pattern recognition of huge data pools collected from sensor networks, image and video systems, online forum platforms, medical agencies etc. However, as an NP hard issue data mining techniques are facing with lots of difficulties. To deal with the hardship, we conduct research on the novel algorithm for data mining and knowledge discovery through network entropy. We firstly introduce necessary data analysis techniques such as support vector machine, neural network and decision tree methods. Later, we analyze the organizational structure of network graphical pattern with the knowledge of machine learning methodology and graph theory. Eventually, our modified method is finalized with decision and validation implementation. The simulation results of our approach on different databases show the feasibility and effectiveness of our proposed framework. As the final part, we provide our conclusion and prospect.
목차
1. Introduction
2. Prior Knowledge on Data Classification
2.1. Decision Tree (DT) based Approach
2.2. Neural Network (NN) based Approach
2.3. Support Vector Machine (SVM) based Approach
2.4. Bayesian Network (BN) based Approach
3. Our Proposed Framework for Data Mining and Knowledge Discovery
3.1. Network Formation Procedure
3.2. Calculation of Network Entropy
3.3. The Regret of Learning Phase
3.4. The Decision and Validation Procedure
4. Experimental Analysis and Simulation
4.1. Environment of the Experiment
4.2. Simulation through the Harvard datasets
4.3. Simulation through the UCI Datasets
5. Conclusion and Summary
References