Data.Mining.Theories.Algorithms.and.Examples.1439808384
Title: Data Mining: Theories, Algorithms, and Examples Author: Nong Ye Length: 349 pages Edition: 1 Language: English Publisher: CRC Press Publication Date: 2013-07-26 ISBN-10: 1439808384 ISBN-13: 9781439808382 New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them. Table of Contents Part I: An Overview of Data Mining Chapter 1: Introduction to Data, Data Patterns, and Data Mining Part II: Algorithms for Mining Classification and Prediction Patterns Chapter 2: Linear and Nonlinear Regression Models Chapter 3: Naïve Bayes Classifier Chapter 4: Decision and Regression Trees Chapter 5: Artificial Neural Networks for Classification and Prediction Chapter 6: Support Vector Machines Chapter 7: k-Nearest Neighbor Classifier and Supervised Clustering Part III: Algorithms for Mining Cluster and Association Patterns Chapter 8: Hierarchical Clustering Chapter 9: K-Means Clustering and Density-Based Clustering Chapter 10: Self-Organizing Map Chapter 11: Probability Distributions of Univariate Data Chapter 12: Association Rules Chapter 13: Bayesian Network Part IV: Algorithms for Mining Data Reduction Patterns Chapter 14: Principal Component Analysis Chapter 15: Multidimensional Scaling Part V: Algorithms for Mining Outlier and Anomaly Patterns Chapter 16: Univariate Control Charts Chapter 17: Multivariate Control Charts Part VI: Algorithms for Mining Sequential and Temporal Patterns Chapter 18: Autocorrelation and Time Series Analysis Chapter 19: Markov Chain Models and Hidden Markov Models Chapter 20: Wavelet Analysis
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