Applied multivariate statistical analysis
Applied multivariate statistical analysis
An empirical study of genetic operators in genetic algorithms
EUROMICRO 93 Nineteenth EUROMICRO symposium on microprocessing and microprogramming on Open system design : hardware, software and applications: hardware, software and applications
Genetic and evolutionary algorithms come of age
Communications of the ACM
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Communications of the ACM
Enhanced genetic operators for the resolution of discrete constrained optimization problems
Computers and Operations Research
Exploiting parallelism in a structural scientific discovery system to improve scalability
Journal of the American Society for Information Science - Special topic issue: youth issues in information science
Data mining: new arsenal for strategic decision-making
Journal of Database Management
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Personalization on the Net using Web mining: introduction
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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Data mining is the process of sifting through the mass of organizational (internal and external) data to identify patterns critical for decision support. Successful implementation of the data mining effort requires a careful assessment of the various tools and algorithms available. The basic premise of this study is that machine-learning algorithms, which are assumption free, should outperform their traditional counterparts when mining business databases. The objective of this study is to test this proposition by investigating the performance of the algorithms for several scenarios. The scenarios are based on simulations designed to reflect the extent to which typical statistical assumptions are violated in the business domain. The results of the computational experiments support the proposition that machine learning algorithms generally outperform their statistical counterparts under certain conditions. These can be used as prescriptive guidelines for the applicability of data mining techniques.