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Introduction to Data Mining: Principles, Methods, and Applications

This summary discusses the principles, methods, and functionalities of data mining systems, focusing on the role of data mining in KDD processes, data preprocessing and cleaning methods, association rules, classification methods, and clustering methods. It covers topics such as data reduction, missing values, outlier elimination, apriori search for frequent patterns, instance-based classification techniques, Bayesian classifiers, decision tree methods, decision rules methods, and clustering algorithms.

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Introduction to Data Mining: Principles, Methods, and Applications

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  1. Summary „Data mining” Vietnam national university inHanoi, College of technology, Feb.2006

  2. Main topics: • Definition, principles and functionalities of data mining systems • Data mining role in KDD processes • Data preprocessing and data cleaning methods • Association rules • Classification methods • Clustering methods

  3. Data preprocessing and data cleaning • Discretization methods • Data reduction methods • Missing values • Outlier elimination

  4. Association rules • Definition, possible applications • Apriori search for frequent patterns and association rules • Modifications of apriori algorithms: hash tree, Apriori-Tid, Apriori-Hybrid • FP-tree method

  5. Classification methods • Instance-based classification techniques • Bayesian classifiers • Decision tree methods • Decision rules methods • Classifier evaluation techniques

  6. Clustering methods • K-means and K-medoids algorithms • Hierarchical clustering • Density clustering

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