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020			_a9780128117606
040			_aCIBESPAM MFL
041			_aeng
082			_a004.3 _bH233
100			_aHan, Jiawei
245			_aData minig: Concepts and techniques.
250			_aFourth Edition
260			_aUnited States _bElsevier, Morgan Kaufmann _c2023
300			_axxix, 752 pages; _bFigures, tables;
505			_a--Chapter 1: Introduction -1.1. What is data mining? -1.2. Data mining: an essential step in knowledge discovery -1.3. Diversity of data types for data mining -1.4. Mining various kinds of knowledge -1.5. Data mining: confluence of multiple disciplines -1.6. Data mining and applications -1.7. Data mining and society -1.8. Summary -1.9. Exercises -1.10. Bibliographic notes -Bibliography --Chapter 2: Data, measurements, and data preprocessing -2.1. Data types -2.2. Statistics of data -2.3. Similarity and distance measures -2.4. Data quality, data cleaning, and data integration -2.5. Data transformation -2.6. Dimensionality reduction -2.7. Summary -2.8. Exercises -2.9. Bibliographic notes -Bibliography --Chapter 3: Data warehousing and online analytical processing -3.1. Data warehouse -3.2. Data warehouse modeling: schema and measures -3.3. OLAP operations -3.4. Data cube computation -3.5. Data cube computation methods -3.6. Summary -3.7. Exercises -3.8. Bibliographic notes -Bibliography --Chapter 4: Pattern mining: basic concepts and methods -4.1. Basic concepts -4.2. Frequent itemset mining methods -4.3. Which patterns are interesting?—Pattern evaluation methods -4.4. Summary -4.5. Exercises -4.6. Bibliographic notes -Bibliography --Chapter 5: Pattern mining: advanced methods -5.1. Mining various kinds of patterns -5.2. Mining compressed or approximate patterns -5.3. Constraint-based pattern mining -5.4. Mining sequential patterns -5.5. Mining subgraph patterns -5.6. Pattern mining: application examples -5.7. Summary -5.8. Exercises -5.9. Bibliographic notes -Bibliography --Chapter 6: Classification: basic concepts and methods -6.1. Basic concepts -6.2. Decision tree induction -6.3. Bayes classification methods -6.4. Lazy learners (or learning from your neighbors) -6.5. Linear classifiers -6.6. Model evaluation and selection -6.7. Techniques to improve classification accuracy -6.8. Summary -6.9. Exercises -6.10. Bibliographic notes -Bibliography --Chapter 7: Classification: advanced methods -7.1. Feature selection and engineering -7.2. Bayesian belief networks -7.3. Support vector machines -7.4. Rule-based and pattern-based classification -7.5. Classification with weak supervision -7.6. Classification with rich data type -7.7. Potpourri: other related techniques -7.8. Summary -7.9. Exercises -7.10. Bibliographic notes -Bibliography --Chapter 8: Cluster analysis: basic concepts and methods -8.1. Cluster analysis -8.2. Partitioning methods -8.3. Hierarchical methods -8.4. Density-based and grid-based methods -8.5. Evaluation of clustering -8.6. Summary -8.7. Exercises -8.8. Bibliographic notes -Bibliography --Chapter 9: Cluster analysis: advanced methods -9.1. Probabilistic model-based clustering -9.2. Clustering high-dimensional data -9.3. Biclustering -9.4. Dimensionality reduction for clustering -9.5. Clustering graph and network data -9.6. Semisupervised clustering -9.7. Summary -9.8. Exercises -9.9. Bibliographic notes -Bibliography --Chapter 10: Deep learning -10.1. Basic concepts -10.2. Improve training of deep learning models -10.3. Convolutional neural networks -10.4. Recurrent neural networks -10.5. Graph neural networks -10.6. Summary -10.7. Exercises -10.8. Bibliographic notes -Bibliography --Chapter 11: Outlier detection -11.1. Basic concepts -11.2. Statistical approaches -11.3. Proximity-based approaches -11.4. Reconstruction-based approaches -11.5. Clustering- vs. classification-based approaches -11.6. Mining contextual and collective outliers -11.7. Outlier detection in high-dimensional data -11.8. Summary -11.9. Exercises -11.10. Bibliographic notes -Bibliography --Chapter 12: Data mining trends and research frontiers -12.1. Mining rich data types -12.2. Data mining applications -12.3. Data mining methodologies and systems -12.4. Data mining, people, and society -Bibliography --Appendix A: Mathematical background -1.1. Probability and statistics -1.2. Numerical optimization -1.3. Matrix and linear algebra -1.4. Concepts and tools from signal processing -1.5. Bibliographic notes -Bibliography -Bibliography -Bibliography -Index
520			_aData Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.
650			_aData Mining
650			_aComputing
650			_aDatabase
700			_aPei, Jian
700			_aTong, Hanghang
912			_c2024-01-04 _dPaúl Villacreses
913			_aTIC _bCC _dSCSAS
942			_2ddc _cBK
999			_c13031 _d13031