The nature of statistical learning theory
The nature of statistical learning theory
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data Mining: the search for knowledge in databases.
Data Mining: the search for knowledge in databases.
Management of uncertainty in Statistical Reasoning: The case of Regression Analysis
International Journal of Approximate Reasoning
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
The fuzzy approach to statistical analysis
Computational Statistics & Data Analysis
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Road crash proneness prediction using data mining
Proceedings of the 14th International Conference on Extending Database Technology
From data to simulation models: component-based model generation with a data-driven approach
Proceedings of the Winter Simulation Conference
A data mining driven risk profiling method for road asset management
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Data Mining (DM) is examined in a statistical perspective, as a methodological area where the objective is to extract useful information from very large databases. It is underlined that DM, as it presently stands, lacks sound theoretical foundations. The main statistical paradigms are briefly reviewed and evaluated with reference to the practice of DM. It is argued that they are insufficient for providing a consistent background to DM activities. The "informational" paradigm is illustrated in general. Some issues concerning design and analysis aspects in DM are discussed within this paradigm. A few examples are illustrated, with reference to the problems of finding association rules in the database, and of setting up appropriate classification procedures.