Statistical analysis with missing data
Statistical analysis with missing data
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
Building neural networks
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
A Search for Hidden Relationships: Data Mining with Genetic Algorithms
Computational Economics - Special issue on computational economics in Geneva: volume 1: computational econometrics, statistics, and optimization
Data mining: building competitive advantage
Data mining: building competitive advantage
Data mining: concepts and techniques
Data mining: concepts and techniques
Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Efficient GA Based Techniques for Classification
Applied Intelligence
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Journal of Management Information Systems - Special section: Data mining
Data Mining
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Data mining is based upon searching the concatenation of multiple databases that usually contain some amount of missing data along with a variable percentage of inaccurate data, pollution, outliers, and noise. The actual data-mining process deals significantly with prediction, estimation, classification, pattern recognition, and the development of association rules. Therefore, the significance of the analysis depends heavily on the accuracy of the database and on the chosen sample data to be used for model training and testing. The issue of missing data must be addressed since ignoring this problem can introduce bias into the models being evaluated and lead to inaccurate data mining conclusions.