DOI QR코드

DOI QR Code

Predicting Discharge Rate of After-care patient using Hierarchy Analysis

  • Received : 2016.05.01
  • Accepted : 2016.05.30
  • Published : 2016.06.30

Abstract

In the growing data saturated world, the question of "whether data can be used" has shifted to "can it be utilized effectively?" More data is being generated and utilized than ever before. As the collection of data increases, data mining techniques also must become more and more accurate. Thus, to ensure this data is effectively utilized, the analysis of the data must be efficient. Interpretation of results from the analysis of the data set presented, have their own on the basis it is possible to obtain the desired data. In the data mining method a decision tree, clustering, there is such a relationship has not yet been fully developed algorithm actually still impact of various factors. In this experiment, the classification method of data mining techniques is used with easy decision tree. Also, it is used special technology of one R and J48 classification technique in the decision tree. After selecting a rule that a small error in the "one rule" in one R classification, to create one of the rules of the prediction data, it is simple and accurate classification algorithm. To create a rule for the prediction, we make up a frequency table of each prediction of the goal. This is then displayed by creating rules with one R, state-of-the-art, classification algorithm while creating a simple rule to be interpreted by the researcher. While the following can be correctly classified the pattern specified in the classification J48, using the concept of a simple decision tree information theory for configuring information theory. To compare the one R algorithm, it can be analyzed error rate and accuracy. One R and J48 are generally frequently used two classifications${\ldots}$

Keywords

References

  1. KHARADE, Kalpana; THAKKAR, Ms Rupal. Promoting ICT enhanced constructivist teaching practices among pre-service teachers: A case study.International Journal of Scientific and Research Publications, 2012, 2: 1-7.
  2. DOGRA, Ashish Kumar; WALA, Tanuj. A Comparative Study of Selected Classification Algorithms of Data Mining. 2015.
  3. SAXENA, Ritika. Educational Data Mining: Performance Evaluation of Decision Tree and Clustering Techniques Using WEKA Platform. International Journal of Computer Science and Business Informatics, IJCSBI. ORG, 2015, 15.2.
  4. SATHYA, R.; GEETHA, M. Kalaiselvi. Vision Based Traffic Personnel Hand Gesture Recognition Using Tree Based Classifiers. In: Computational Intelligence in Data Mining-Volume 2. Springer India, 2015. p. 187-200.
  5. WAGH, Sharmila, et al. Effective Framework of J48 Algorithm using Semi-Supervised Approach for Intrusion Detection. International Journal of Computer Applications, 2014, 94.12. https://doi.org/10.5120/16396-6015