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Research on Pattern Analysis and Data Classification Methodology for Data Mining and Knowledge Discovery
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- 발행기관
- 보안공학연구지원센터(IJHIT) 바로가기
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- 간행물
- International Journal of Hybrid Information Technology 바로가기
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- 통권
- Vol.9 No.3 (2016.03)바로가기
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- 페이지
- pp.179-188
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A270785
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
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
Abstract
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
키워드
Pattern Analysis and Machine Intelligence Data Classification Technique Data Mining Knowledge Discovery Big Data and Information Security저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJHIT) [Science & Engineering Research Support Center, Republic of Korea(IJHIT)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Hybrid Information Technology
- 간기
- 격월간
- pISSN
- 1738-9968
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Hybrid Information Technology Vol.9 No.3
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