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Data mining techniques (Record no. 14383)

MARC details
000 -LEADER
fixed length control field	05500nam a22002057a 4500
005 - DATE & TIME
control field	20260216094710.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	260216b \|\|\|\|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - ISBN
International Standard Book Number	9789386235053
Price	950.00
040 ## - CATALOGING SOURCE
Original cataloging agency	S.X.U.K
041 ## - Language
Language	English
082 ## - DDC NUMBER
Classification number	R 006.312 PUJ(DAT)Ed4
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Pujari, K. Arun
245 ## - TITLE STATEMENT
Title	Data mining techniques
Statement of responsibility	Arun K. Pujari
250 ## - EDITION STATEMENT
Edition statement	4th ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc	Kolkata
Name of publisher, distributor, etc	University press
Date of publication, distribution, etc	2024
300 ## - PHYSICAL DESCRIPTION
Pages	407p.
Other Details	P.B.
500 ## - GENERAL NOTE
General note	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/>
650 ## - Subject
Subject	Data mining
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	REFERENCE COMPUTER SCIENCE

Holdings
Source of classification or shelving scheme	Not for loan	Koha collection	Location (home branch)	Sublocation or collection (holding branch)	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Serial Enumeration / chronology	Koha full call number	Barcode (Accession No.)	Koha date last seen	Copy Number	Price effective from	Koha item type
Dewey Decimal Classification	Not For Loan	Reference	St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Reference Section	02/16/2026	K.M. Enterprise	950.00	S.X.U.K	R 006.312 PUJ(DAT)Ed4	UCS13955	02/16/2026	13955	02/16/2026	REFERENCE COMPUTER SCIENCE
Dewey Decimal Classification			St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Lending Section	02/16/2026	K.M. Enterprise	950.00	S.X.U.K	006.312 PUJ(DAT)Ed4	CS13956	02/16/2026	13956	02/16/2026	COMPUTER SCIENCE
Dewey Decimal Classification			St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Lending Section	02/16/2026	K.M. Enterprise	950.00	S.X.U.K	006.312 PUJ(DAT)Ed4.C1	CS13957	02/16/2026	13957	02/16/2026	COMPUTER SCIENCE