Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Calibrating the COCOMO II post-architecture model
Proceedings of the 20th international conference on Software engineering
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Learning and Practicing Econometrics: SAS Handbook
Learning and Practicing Econometrics: SAS Handbook
Software Engineering Economics
Software Engineering Economics
Principles of Corporate Finance with Cdrom
Principles of Corporate Finance with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Knowledge Discovery in Databases
Knowledge Discovery in Databases
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
Bayesian analysis of software cost and quality models
Bayesian analysis of software cost and quality models
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Toward data mining engineering: A software engineering approach
Information Systems
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
Investigating the relationship among self-leadership strategies by association rules mining
International Journal of Business Information Systems
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CRISP-DM is the standard to develop Data Miningprojects. CRISP-DM proposes processes and tasks that you have to carry out to develop a Data Miningproject. A task proposed by CRISP-DM is the cost estimation of the Data Miningproject. In software development a lot of methods are described to estimate the costs of project development (SLIM, SEER-SEM, PRICE-S and COCOMO). These methods are not appropriate in the case of Data Miningprojects because in Data Miningsoftware development is not the first goal. Some methods have been proposed to estimate some phases of a Data Miningproject, but there is no method to estimate the global cost of a generic Data Miningproject. The lack of Data Miningproject estimation methods is because of many real-life project failures due to the non-realistic estimation at the beginning of the projects. Consequently, in this paper we propose to design and validate a parametric cost estimation model, similar to COCOMO or SLIM in software development, for Data Miningprojects (DMCoMo). The drivers of the model will be proposed first and later the equation of the model will be proposed.