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Data Mining (Record no. 8800)

MARC details
000 -LEADER
fixed length control field	04514nam a22001577a 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

Holdings
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	Price effective from	Koha item type
		Dewey Decimal Classification			Non-fiction	Tetso College Library	Tetso College Library	Computer Science	08/03/2024	687.00		006.3 HAN	13426	08/03/2024	08/03/2024	Books