Evaluating the success level of data mining projects based on CRISP-DM methodology by a Fuzzy expert system

Autor:	Hamid Eslami Nosratabadi, Elham Naghizadeh Kakhky, Ahmad Nadali
Rok vydání:	2011
Předmět:	Adaptive neuro fuzzy inference system Neuro-fuzzy Computer science business.industry Process (engineering) Legal expert system computer.software_genre Expert system ComputingMethodologies_PATTERNRECOGNITION Empirical research Software Fuzzy set operations Data mining business computer
Zdroj:	2011 3rd International Conference on Electronics Computer Technology.
Popis:	One of the critical issues in data mining process especially for organizations is evaluating the success level of performed data mining projects. The purpose of this research is designing a Fuzzy expert system for the evaluation of success level of data mining projects based on quality of CRISP-DM methodology phases as one of the famous data mining methodologies. Here the CRISP-DM phases are specified as inputs of Fuzzy Inference System (FIS) model and the output is the success level of data mining project. This system has been designed by MATLAB software and has been implemented for a data mining project in an Iranian Bank as empirical study.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::89995b3bfa80684da2c1007da7f952f7 https://doi.org/10.1109/icectech.2011.5942073 Zobrazit plný text záznamu