A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining

Background and Objectives: Nowadays, data mining is one of the most significant issues. One field of data mining is a mixture of computer science and statistics which is considerably limited due to increase in digital data and growth of computational power of computers. One of the domains of data mi...

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Main Authors: A. Saberi nejad, R. Tavoli
Format: Article
Language:English
Published: Shahid Rajaee Teacher Training University 2017-12-01
Series:Journal of Electrical and Computer Engineering Innovations
Subjects:
Online Access:https://jecei.sru.ac.ir/article_811_ef917f136aefb0ba185c96c3babbf7bb.pdf
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author A. Saberi nejad
R. Tavoli
author_facet A. Saberi nejad
R. Tavoli
author_sort A. Saberi nejad
collection DOAJ
description Background and Objectives: Nowadays, data mining is one of the most significant issues. One field of data mining is a mixture of computer science and statistics which is considerably limited due to increase in digital data and growth of computational power of computers. One of the domains of data mining is the software cost estimation category. Methods: In this article, classifying techniques of learning algorithm of machine and COCOMO model as the most common estimation model of software costs are presented. Then, the analysis method of principal component approach is presented. Results: This article presents a suitable method to improve the performance of the software cost estimation. Moreover, the basic data set is decreased and is turned into a new collection by using this method. Among the features, the best are extracted. The algorithms of several classifications are assessed by applying this method. Finally, the evidence for accuracy of our claims in terms of increase in estimation accuracy of software costs is presented. Conclusion:. The results proved that the suggested method could have significant influence on models of decision tree, naïve Bayes and nearest neighborhood by decreasing dimension of input data and turning it into data. ======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
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spelling doaj.art-8193d267d4ca4e8993b500bcc0c323a22022-12-22T00:38:36ZengShahid Rajaee Teacher Training UniversityJournal of Electrical and Computer Engineering Innovations2322-39522345-30442017-12-0161334210.22061/jecei.2018.811811A Method for Estimating the Cost of Software Using Principle Components Analysis and Data MiningA. Saberi nejad0R. Tavoli1MSc student of computer engineering – software, Pooyandegan Danesh Institution of Higher Education, Chalus, IranFull time science Committee member, Islamic Azad University of chalus, Chalus, IranBackground and Objectives: Nowadays, data mining is one of the most significant issues. One field of data mining is a mixture of computer science and statistics which is considerably limited due to increase in digital data and growth of computational power of computers. One of the domains of data mining is the software cost estimation category. Methods: In this article, classifying techniques of learning algorithm of machine and COCOMO model as the most common estimation model of software costs are presented. Then, the analysis method of principal component approach is presented. Results: This article presents a suitable method to improve the performance of the software cost estimation. Moreover, the basic data set is decreased and is turned into a new collection by using this method. Among the features, the best are extracted. The algorithms of several classifications are assessed by applying this method. Finally, the evidence for accuracy of our claims in terms of increase in estimation accuracy of software costs is presented. Conclusion:. The results proved that the suggested method could have significant influence on models of decision tree, naïve Bayes and nearest neighborhood by decreasing dimension of input data and turning it into data. ======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================https://jecei.sru.ac.ir/article_811_ef917f136aefb0ba185c96c3babbf7bb.pdfincreased accuracysoftware cost estimationprinciple components analysisdata mining
spellingShingle A. Saberi nejad
R. Tavoli
A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining
Journal of Electrical and Computer Engineering Innovations
increased accuracy
software cost estimation
principle components analysis
data mining
title A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining
title_full A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining
title_fullStr A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining
title_full_unstemmed A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining
title_short A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining
title_sort method for estimating the cost of software using principle components analysis and data mining
topic increased accuracy
software cost estimation
principle components analysis
data mining
url https://jecei.sru.ac.ir/article_811_ef917f136aefb0ba185c96c3babbf7bb.pdf
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