Data Mining (Record no. 8800)
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000 -LEADER | |
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fixed length control field | 04416nam a22001697a 4500 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 240308b |||||||| |||| 00| 0 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 978-81-312-6766-0 |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Edition number | 23 |
Classification number | 006.3 |
Item number | HAN |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | Han Jiawei |
245 ## - TITLE STATEMENT | |
Title | Data Mining |
Remainder of title | Concepts and Techniques |
Statement of responsibility, etc. | Jiawin Han, Jian Pei & Hanghang Tong |
Medium | English |
250 ## - EDITION STATEMENT | |
Edition statement | 4th ed |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication, distribution, etc. | New Delhi |
Name of publisher, distributor, etc. | Elsevier |
Date of publication, distribution, etc. | 2022 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | vii, 752 pages. ; |
Other physical details | soft bound |
Dimensions | 18x24 cm |
505 ## - FORMATTED CONTENTS NOTE | |
FORMATTED CONTENTS NOTE | Chapter 1: Introduction<br/>1.1. What is data mining?<br/>1.2. Data mining: an essential step in knowledge discovery<br/>1.3. Diversity of data types for data mining<br/>1.4. Mining various kinds of knowledge<br/>1.5. Data mining: confluence of multiple disciplines<br/>1.6. Data mining and applications<br/>1.7. Data mining and society<br/>1.8. Summary<br/>1.9. Exercises<br/>1.10. Bibliographic notes<br/>Bibliography<br/>Chapter 2: Data, measurements, and data preprocessing<br/>2.1. Data types<br/>2.2. Statistics of data<br/>2.3. Similarity and distance measures<br/>2.4. Data quality, data cleaning, and data integration<br/>2.5. Data transformation<br/>2.6. Dimensionality reduction<br/>2.7. Summary<br/>2.8. Exercises<br/>2.9. Bibliographic notes<br/>Bibliography<br/>Chapter 3: Data warehousing and online analytical processing<br/>3.1. Data warehouse<br/>3.2. Data warehouse modeling: schema and measures<br/>3.3. OLAP operations<br/>3.4. Data cube computation<br/>3.5. Data cube computation methods<br/>3.6. Summary<br/>3.7. Exercises<br/>3.8. Bibliographic notes<br/>Bibliography<br/>Chapter 4: Pattern mining: basic concepts and methods<br/>4.1. Basic concepts<br/>4.2. Frequent itemset mining methods<br/>4.3. Which patterns are interesting?—Pattern evaluation methods<br/>4.4. Summary<br/>4.5. Exercises<br/>4.6. Bibliographic notes<br/>Bibliography<br/>Chapter 5: Pattern mining: advanced methods<br/>5.1. Mining various kinds of patterns<br/>5.2. Mining compressed or approximate patterns<br/>5.3. Constraint-based pattern mining<br/>5.4. Mining sequential patterns<br/>5.5. Mining subgraph patterns<br/>5.6. Pattern mining: application examples<br/>5.7. Summary<br/>5.8. Exercises<br/>5.9. Bibliographic notes<br/>Bibliography<br/>Chapter 6: Classification: basic concepts and methods<br/>6.1. Basic concepts<br/>6.2. Decision tree induction<br/>6.3. Bayes classification methods<br/>6.4. Lazy learners (or learning from your neighbors)<br/>6.5. Linear classifiers<br/>6.6. Model evaluation and selection<br/>6.7. Techniques to improve classification accuracy<br/>6.8. Summary<br/>6.9. Exercises<br/>6.10. Bibliographic notes<br/>Bibliography<br/>Chapter 7: Classification: advanced methods<br/>7.1. Feature selection and engineering<br/>7.2. Bayesian belief networks<br/>7.3. Support vector machines<br/>7.4. Rule-based and pattern-based classification<br/>7.5. Classification with weak supervision<br/>7.6. Classification with rich data type<br/>7.7. Potpourri: other related techniques<br/>7.8. Summary<br/>7.9. Exercises<br/>7.10. Bibliographic notes<br/>Bibliography<br/>Chapter 8: Cluster analysis: basic concepts and methods<br/>8.1. Cluster analysis<br/>8.2. Partitioning methods<br/>8.3. Hierarchical methods<br/>8.4. Density-based and grid-based methods<br/>8.5. Evaluation of clustering<br/>8.6. Summary<br/>8.7. Exercises<br/>8.8. Bibliographic notes<br/>Bibliography<br/>Chapter 9: Cluster analysis: advanced methods<br/>9.1. Probabilistic model-based clustering<br/>9.2. Clustering high-dimensional data<br/>9.3. Biclustering<br/>9.4. Dimensionality reduction for clustering<br/>9.5. Clustering graph and network data<br/>9.6. Semisupervised clustering<br/>9.7. Summary<br/>9.8. Exercises<br/>9.9. Bibliographic notes<br/>Bibliography<br/>Chapter 10: Deep learning<br/>10.1. Basic concepts<br/>10.2. Improve training of deep learning models<br/>10.3. Convolutional neural networks<br/>10.4. Recurrent neural networks<br/>10.5. Graph neural networks<br/>10.6. Summary<br/>10.7. Exercises<br/>10.8. Bibliographic notes<br/>Bibliography<br/>Chapter 11: Outlier detection<br/>11.1. Basic concepts<br/>11.2. Statistical approaches<br/>11.3. Proximity-based approaches<br/>11.4. Reconstruction-based approaches<br/>11.5. Clustering- vs. classification-based approaches<br/>11.6. Mining contextual and collective outliers<br/>11.7. Outlier detection in high-dimensional data<br/>11.8. Summary<br/>11.9. Exercises<br/>11.10. Bibliographic notes<br/>Bibliography<br/>Chapter 12: Data mining trends and research frontiers<br/>12.1. Mining rich data types<br/>12.2. Data mining applications<br/>12.3. Data mining methodologies and systems<br/>12.4. Data mining, people, and society<br/>Bibliography<br/>Appendix A: Mathematical background<br/>1.1. Probability and statistics<br/>1.2. Numerical optimization<br/>1.3. Matrix and linear algebra<br/>1.4. Concepts and tools from signal processing<br/>1.5. Bibliographic notes |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | Dewey Decimal Classification |
Koha item type | Books |
Koha issues (borrowed), all copies | 1 |
Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
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Dewey Decimal Classification | Non-fiction | Tetso College Library | Tetso College Library | Computer Science | 08/03/2024 | 687.00 | 1 | 006.3 HAN | 13426 | 18/02/2025 | 30/01/2025 | 08/03/2024 | Books |