Venkata Naresh Mandhala, Lakshmipathi Anantha, Vijay Krishna Dhulipalla, Hye-Jin Kim
언어
영어(ENG)
URL
https://www.earticle.net/Article/A285062
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
영어
The commitment of measurements to information mining can be followed back to the work by Bayes in 1763. The business organizations gather information and offer it to the Data Marts. The individuals who run little and medium association needs to set up information warehousing to touch base, best case scenario arrangement. Such datasets contain part of missing qualities, at some point the missing qualities range from 10% to 33%. A portion of the information might be fundamental; to recall such information is a troublesome undertaking and this kind of datasets won't yield better arrangement, to take care of this issue the Expectation Maximization (EM) calculation gauges missing qualities. Utilizing EM Algorithm the outcomes are supplanted in the missing positions of the specific information which serves to exact conclusion. In this paper, point estimators were connected, among which EM calculation gives best gauge. It is watched that the more straightforward models by and large yield the best results.
목차
Abstract 1. Introduction 1.1. Parametric Models 1.2. Non-Parametric Procedures 1.3. Organization of this Paper 2. Statistical Data Mining 2.1. Point Estimation 2.2. Mean Squared Error (MSE) Method 2.3. Root Mean Square (RMS) 2.4. Interval Estimate 2.5. Maximum Likelihood Estimate (MLE) 3. Future Scope of the Paper 4. Conclusion References
키워드
Data MartsExpectation MaximizationMachine LearningData Mining
저자
Venkata Naresh Mandhala [ Department of Computer Science and Engineering, KL University, Vaddeswaram, AP, 522502, India ]
Lakshmipathi Anantha [ Department of Information Technology, VFSTR University, Vadlamudi-522213, Guntur, India ]
Vijay Krishna Dhulipalla [ Department of Management Studies, VFSTR University, Vadlamudi-522213, Guntur, India ]
Hye-Jin Kim [ Sungshin W. University, 2,Bomun-ro 34da-gil, 0Seongbuk-gu, Seoul ]
Corresponding author
보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
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
간행물명
International Journal of u- and e- Service, Science and Technology
간기
격월간
pISSN
2005-4246
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.9 No.9