Volume 14 Number 1 (Jan. 2019)
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JSW 2019 Vol.14(1): 36-46 ISSN: 1796-217X
doi: 10.17706/jsw.14.1.36-46

AMPM3 Criteria of Algorithm Summation for Classifying Datamining of Software Quality Management

Kattiya T. Yangyuen1*, Tipaporn Suppamit2, Surasak Mungsing2
1 Rattana Bundit University, 306 Ladproud Rd., Bangkapi, Bangkok, Thailand, 10240. (Thai)
2 School of Information Technology, Sripatum University, 2410, 2 Phahonytthin Rd., Senanikhon, Chatuchuck, Thailand, 10900 (Thai)

Abstract— This research has the objective to present the algorithm summation method of AMPM3 criteria for reducing attributes in data mining classification of software quality management. Moreover, the programs used to analyze in this research are WE-KA and MATLAB and the techniques used to predict the equation and the accuracy are Decision Tree, Rule-Based, Naïve Bayesian and KNN. Besides, the results of analyzing the Algorithm with AMPM3 criteria are to reduce the attributes from searching from the relationship of regression analyzing and the regression analysis that are the analyzing from the relationships between 2 variables for finding the similarities between questions and model documents by searching from the patterns of designing. Then, it has the steps as these following:1) Preparation of Information2) Selection of Information3) Practicing Information Set and Test 4) Processing of Information 5) Creation of Model for analyzing the information relationship and effectiveness measurement to reduce attributions.

Index Terms— Reducing of attributes, classifying of information, software quality management.


Cite: Kattiya T. Yangyuen, Tipaporn Suppamit, Surasak Mungsing, "AMPM3 Criteria of Algorithm Summation for Classifying Datamining of Software Quality Management," Journal of Software vol. 14, no. 1, pp. 36-46, 2019.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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