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Preface | |
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Data Mining Concepts | |
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Introduction | |
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Data-mining roots | |
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Data-mining process | |
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Large data sets | |
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Data warehouses | |
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Organization of this book | |
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Review questions and problems | |
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References for further study | |
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Preparing the Data | |
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Representation of raw data | |
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Characteristics of raw data | |
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Transformation of raw data | |
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Missing data | |
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Time-dependent data | |
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Outlier analysis | |
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Review questions and problems | |
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References for further study | |
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Data Reduction | |
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Dimensions of large data sets | |
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Features reduction | |
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Entropy measure for ranking features | |
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Principal component analysis | |
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Values reduction | |
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Feature discretization: ChiMerge technique | |
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Cases reduction | |
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Review questions and problems | |
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References for further study | |
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Learning from Data | |
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Learning machine | |
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Statistical learning theory | |
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Types of learning methods | |
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Common learning tasks | |
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Model estimation | |
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Review questions and problems | |
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References for further study | |
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Statistical Methods | |
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Statistical inference | |
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Assessing differences in data sets | |
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Bayesian inference | |
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Predictive regression | |
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Analysis of variance | |
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Logistic regression | |
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Log-linear models | |
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Linear discriminant analysis | |
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Review questions and problems | |
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References for further study | |
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Cluster Analysis | |
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Clustering concepts | |
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Similarity measures | |
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Agglomerative hierarchical clustering | |
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Partitional clustering | |
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Incremental clustering | |
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Review questions and problems | |
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References for further study | |
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Decision Trees and Decision Rules | |
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Decision trees | |
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C4.5 Algorithm: generating a decision tree | |
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Unknown attribute values | |
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Pruning decision tree | |
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C4.5 Algorithm: generating decision rules | |
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Limitations of decision trees and decision rules | |
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Associative-classification method | |
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Review questions and problems | |
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References for further study | |
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Association Rules | |
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Market-Basket Analysis | |
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Algorithm Apriori | |
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From frequent itemsets to association rules | |
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Improving the efficiency of the Apriori algorithm | |
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Frequent pattern-growth method | |
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Multidimensional association-rules mining | |
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Web mining | |
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HITS and LOGSOM algorithms | |
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Mining path-traversal patterns | |
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Text mining | |
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Review questions and problems | |
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References for further study | |
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Artificial Neural Networks | |
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Model of an artificial neuron | |
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Architectures of artificial neural networks | |
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Learning process | |
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Learning tasks | |
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Multilayer perceptrons | |
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Competitive networks and competitive learning | |
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Review questions and problems | |
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References for further study | |
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Genetic Algorithms | |
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Fundamentals of genetic algorithms | |
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Optimization using genetic algorithms | |
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A simple illustration of a genetic algorithm | |
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Schemata | |
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Traveling salesman problem | |
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Machine learning using genetic algorithms | |
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Review questions and problems | |
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References for further study | |
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Fuzzy Sets and Fuzzy Logic | |
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Fuzzy sets | |
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Fuzzy set operations | |
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Extension principle and fuzzy relations | |
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Fuzzy logic and fuzzy inference systems | |
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Multifactorial evaluation | |
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Extracting fuzzy models from data | |
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Review questions and problems | |
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References for further study | |
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Visualization Methods | |
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Perception and visualization | |
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Scientific visualization and information visualization | |
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Parallel coordinates | |
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Radial visualization | |
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Kohonen self-organized maps | |
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Visualization systems for data mining | |
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Review questions and problems | |
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References for further study | |
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References | |
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Data-Mining Tools | |
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Commercially and publicly available tools | |
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Web site links | |
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Data-Mining Applications | |
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Data mining for financial data analysis | |
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Data mining for the telecommunications industry | |
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Data mining for the retail industry | |
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Data mining in healthcare and biomedical research | |
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Data mining in science and engineering | |
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Pitfalls of data mining | |
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Index | |
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About the Author | |