MARC details
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06849nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20221122160347.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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221122b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789386235053 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.741 |
| Item number |
PUJ |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Pujari, Arun K. |
| 245 ## - TITLE STATEMENT |
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| Title |
Data mining techniques |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
4th |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
University Press |
| Place of publication, distribution, etc. |
Hyderabad |
| Date of publication, distribution, etc. |
2020 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 407 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
INR |
| Price amount |
625.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Table of content<br/><br/>Foreword xv<br/>Prologue xvii<br/>Preface to the Fourth Edition xix<br/>Preface to the First Edition xxi<br/>Acknowledgements<br/>1. INTRODUCTION<br/>1.1 Introduction 1.2 Data Mining as a Subject<br/>1.3 Guide to this Book<br/>2. DATA WAREHOUSING<br/>2.1 Introduction<br/>2.2 Data Warehouse Architecture<br/>2.3 Dimensional Modelling<br/>2.4 Categorisation of Hierarchies 2.5 Aggregate Function<br/>2.6 Summarisability<br/>2.7 Fact–Dimension Relationships<br/>2.8 OLAP Operations<br/>2.9 Lattice of Cuboids<br/>2.10 OLAP Server<br/>2.11 ROLAP<br/>2.12 MOLAP<br/>2.13 Cube Computation<br/>2.14 Multiway Simultaneous Aggregation (ArrayCube)<br/>2.15 BUC - Bottom-Up Cubing Algorithm<br/>2.16 Condensed Cube<br/>2.17 Coalescing<br/>2.18 Dwarf<br/>2.19 Other Cubing Techniques<br/>2.20 Skycube<br/>2.21 View Selection - Partial Materialisation<br/>2.22 Data Marting<br/>2.23 ETL<br/>2.24 Data Cleaning<br/>2.25 ELT vs. ETL<br/>2.26 Cloud Data Warehousing Further Reading<br/>Exercises<br/>Bibliography<br/>3. DATA MINING<br/>3.1 Introduction<br/>3.2 What is Data Mining?<br/>3.3 Data Mining: Definitions<br/>3.4 KDD vs. Data Mining <br/>3.5 DBMS vs. DM <br/>3.6 Other Related Areas <br/>3.7 DM Techniques <br/>3.8 Other Mining Problems <br/>3.9 Issues and Challenges in DM <br/>3.10 DM Application Areas <br/>3.11 DM Applications—Case Studies <br/>3.12 Conclusions <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>4. ASSOCIATION RULES <br/>4.1 Introduction <br/>4.2 What is an Association Rule? <br/>4.3 Methods to Discover Association Rules <br/>4.4 Apriori Algorithm <br/>4.5 Partition Algorithm <br/>4.6 Pincer-Search Algorithm <br/>4.7 Dynamic Itemset Counting Algorithm <br/>4.8 FP-tree Growth Algorithm <br/>4.9 Eclat and dEclat <br/>4.10 Rapid Association Rule Mining (RARM) <br/>4.11 Discussion on Different Algorithms <br/>4.12 Incremental Algorithm <br/>4.13 Border Algorithm <br/>4.14 Generalised Association Rule<br/>4.15 Association Rules with Item Constraints <br/>4.16 Summary <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>5. CLUSTERING TECHNIQUES <br/>5.1 Introduction <br/>5.2 Clustering Paradigms <br/>5.3 Partitioning Algorithms <br/>5.4 k-Medoid Algorithms <br/>5.5 CLARA <br/>5.6 CLARANS <br/>5.7 Hierarchical Clustering <br/>5.8 DBSCAN <br/>5.9 BIRCH <br/>5.10 CURE <br/>5.11 Categorical Clustering Algorithms <br/>5.12 STIRR <br/>5.13 ROCK <br/>5.14 CACTUS <br/>5.15 Conclusions <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>6. DECISION TREES <br/>6.1 Introduction <br/>6.2 What is a Decision Tree? <br/>6.3 Tree Construction Principle <br/>6.4 Best Split <br/>6.5 Splitting Indices <br/>6.6 Splitting Criteria <br/>6.7 Decision Tree Construction Algorithms <br/>6.8 CART <br/>6.9 ID3 <br/>6.10 C4.5 <br/>6.11 CHAID <br/>6.12 Summary <br/>6.13 Decision Tree Construction with Presorting <br/>6.14 RainForest <br/>6.15 Approximate Methods <br/>6.16 CLOUDS <br/>6.17 BOAT <br/>6.18 Pruning Technique <br/>6.19 Integration of Pruning and Construction <br/>6.20 Summary: An Ideal Algorithm <br/>6.21 Other Topics <br/>6.22 Conclusions <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>7. ROUGH SET THEORY <br/>7.1 Introduction <br/>7.2 Definitions <br/>7.3 Example <br/>7.4 Reduct <br/>7. 5 Propositional Reasoning and PIAP to Compute Reducts <br/>7.6 Types of Reducts <br/>7.7 Rule Extraction <br/>7.8 Decision tree <br/>7.9 Rough Sets and Fuzzy Sets <br/>7.10 Granular Computing <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>8. GENETIC ALGORITHM <br/>8.1 Introduction <br/>8.2 Basic Steps of GA <br/>8. 3 Selection <br/>8.4 Crossover <br/>8.5 Mutation <br/>8.6 Data Mining Using GA <br/>8.7 GA for Rule Discovery <br/>8.8 GA and Decision Tree <br/>8.9 Clustering Using GA <br/>Conclusions <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>9. OTHER TECHNIQUES <br/>9.1 Introduction <br/>9.2 What is a Neural Network? <br/>9.3 Learning in NN <br/>9.4 Unsupervised Learning <br/>9.5 Data Mining Using NN: A Case Study <br/>9.6 Support Vector Machines <br/>9.7 Conclusions <br/>Further Reading <br/>Exercises <br/>Bibliography <br/><br/>10. Performance Evaluation - ROC Curve<br/>10.1 Introduction<br/>10.2 Classification Accuracy<br/>10.3 ROC Space<br/>10.4 ROC Curves<br/>10.5 ROC Curves and Class Distribution<br/>10.6 ROC Convex Hull (ROCCH)<br/>10.7 Method to Find the Optimal Threshold Point<br/>10.8 Combining Classifiers<br/>10.9 Area Under the ROC Curve (AUC )<br/>10.10 Methods to Compute AUC <br/>10.11 Averaging ROC Curves<br/>10.12 R OC for Multi-class Classifiers<br/>10.13 Precision–Recall Graph<br/>10.14 DET Curves<br/>10.15 Cost Curves<br/>Further Reading<br/>Exercises<br/>Bibliography<br/>11. WEB MINING <br/>11.1 Introduction <br/>11.2 Web Mining <br/>11.3 Web Content Mining <br/>11.4 Web Structure Mining <br/>11.5 Web Usage Mining <br/>11.6 Text Mining <br/>11.7 Unstructured Text <br/>11.8 Episode Rule Discovery for Texts <br/>11.9 Hierarchy of Categories <br/>11.10 Text Clustering <br/>11.11 Conclusions <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>12. TEMPORAL AND SPATIAL DATA MINING <br/>12.1 Introduction <br/>12.2 What is Temporal Data Mining? <br/>12.3 Temporal Association Rules <br/>12.4 Sequence Mining <br/>12.5 The GSP Algorithm <br/>12.6 SPADE <br/>12.7 SPIRIT <br/>12.8 WUM <br/>12.9 Episode Discovery <br/>12.10 Event Prediction Problem <br/>12.11 Time-series Analysis <br/>12.12 Spatial Mining <br/>12.13 Spatial Mining Tasks <br/>12.14 Spatial Clustering <br/>12.15 Spatial Trends <br/>12.16 Conclusions <br/>Further Reading <br/>Exercises <br/>Bibliography <br/>Index<br/><br/> |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
This book addresses all the major and latest techniques of data mining. It deals in detail with the algorithms for discovering association rules for clustering and building decision trees, and techniques such as neural networks, genetic algorithms, rough set theory and support vector machine used in data mining. The algorithmic details of different techniques such as Apriori, Pincer-search, Dynamic Itemset Counting, FP-Tree growth, SLIQ, SPRINT, BOAT, CART, RainForest, BIRCH, CURE, BUBBLE, ROCK, STIRR, PAM, CLARANS, DBSCAN, GSP, SPADE and SPIRIT are covered. The book also discusses the mining of web, spatial, temporal and text data. In the third edition, the chapter on data warehousing concepts was thoroughly revised to include multidimensional data modelling and cube computation. The discussion on genetic algorithms was also expanded as a separate chapter. In the fourth edition, a chapter on ROC curve for visualizing the performance of a binary classifier and the method for computing AUC and its uses has been included.<br/><br/>Students of computer science, mathematical science and management will find this introductory textbook beneficial for a first course on the subject; the exposition of concepts with supporting illustrative examples and exercises makes it suitable for self-study as well. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Information retrieval |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |