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
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