St. Xavier's University Library Search for 'su:"DATA MINING"'

Data mining for business intelligence : concepts, techniques and applications in Microsoft office excel with XLMiner

]]> By Shmueli, Galit.
New Delhi Wiley India Pvt.Ltd. 2007 .
279 , Includes index 9788126517589

]]> Place hold on Data mining for business intelligence

Data mining for business intelligence : concepts, techniques and applications in Microsoft office excel with XLMiner

]]> By Shmueli, Galit.
New Delhi Wiley India Pvt.Ltd. 2007 .
279 , Includes index 9788126517589

]]> Place hold on Data mining for business intelligence

Data mining : introductory and advance topies

]]> By Dunham, Margaret H..
Noida Pearson 2012 .
311p. , includes index c2006.
9788177587852

]]> Place hold on Data mining

Data science for business

]]> By Provost, Foster .
New Delhi Shroff publishers & distributors pvt. ltd. 2013 .
xviii, 384 , Includes index, glossary & bibliography 9789351102670

]]> Place hold on Data science for business

Introduction to Data mining

]]> Noida Pearson 2021 .
856 , Includes index & bibliographic notes 9789354491047

]]> Place hold on Introduction to Data mining

The elements of statistical learning : data mining, inference, and prediction /

]]> By Hastie, Trevor..
New York, NY : Springer, 2009 .
xxii, 745 p. : , Introduction Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 1-8 Overview of Supervised Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 9-41 Linear Methods for Regression Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 43-99 Linear Methods for Classification Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 101-137 Basis Expansions and Regularization Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 139-189 Kernel Smoothing Methods Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 191-218 Model Assessment and Selection Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 219-259 Model Inference and Averaging Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 261-294 Additive Models, Trees, and Related Methods Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 295-336 Boosting and Additive Trees Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 337-387 Neural Networks Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 389-416 Support Vector Machines and Flexible Discriminants Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 417-458 Prototype Methods and Nearest-Neighbors Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 459-483 Unsupervised Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 485-585 Random Forests Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 587-604 Ensemble Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 605-624 Undirected Graphical Models Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 625-648 High-Dimensional Problems: p N Trevor Hastie, Robert Tibshirani, Jerome Friedman Pages 649-698 Back Matter 25 cm..
9780387848570 (hardcover : alk. paper) | 9780387848587 (electronic)

]]> Place hold on The elements of statistical learning :

Introduction to data mining

By Tan, Pang-Ning.
Noida Pearson 2020 .
760p. , includes index 978933257140

]]> Place hold on Introduction to data mining

Data mining and predictive analytics : An Indian adaptation

]]> By Larose, Daniel T. .
New Delhi Wiley 2022 .
xxxiv, 871 , Includes index & appendix 9789354247255

]]> Place hold on Data mining and predictive analytics

Data mining and data warehousing : principles and practical techniques

]]> By Bhatia, Parteek .
New Delhi Cambridge university press 2019 .
xxxiv, 477 9781108727747

]]> Place hold on Data mining and data warehousing

Media analytics : understanding media, audiences, and consumers in the 21st century /

]]> By Hollifield, C. Ann,.
New York: Routledge, 2022 .
xxi, 416 p. , INCLUDES GLOSSARY, INDEX 9781138581050

]]> Place hold on Media analytics :

Media analytics : understanding media, audiences, and consumers in the 21st century

]]> By Hollifield, C. Ann,.
New York: Routledge, 2022 .
xxi, 416 p. , Includes glossary and index 23 cm..
9781138581050

]]> Place hold on Media analytics

Synthetic media : navigating the futur of ai and ml generated content, opportunities, threats and the future of humanity

]]> Kolkata Mitra 2025 .
462p. 9789334224290

]]> Place hold on Synthetic media

Data mining techniques

]]> By Pujari, K. Arun.
Kolkata University press 2024 .
407p. , Foreword xv Prologue xvii Preface to the Fourth Edition xix Preface to the First Edition xxi Acknowledgements 1. INTRODUCTION 1.1 Introduction 1.2 Data Mining as a Subject 1.3 Guide to this Book 2. DATA WAREHOUSING 2.1 Introduction 2.2 Data Warehouse Architecture 2.3 Dimensional Modelling 2.4 Categorisation of Hierarchies 2.5 Aggregate Function 2.6 Summarisability 2.7 Fact–Dimension Relationships 2.8 OLAP Operations 2.9 Lattice of Cuboids 2.10 OLAP Server 2.11 ROLAP 2.12 MOLAP 2.13 Cube Computation 2.14 Multiway Simultaneous Aggregation (ArrayCube) 2.15 BUC - Bottom-Up Cubing Algorithm 2.16 Condensed Cube 2.17 Coalescing 2.18 Dwarf 2.19 Other Cubing Techniques 2.20 Skycube 2.21 View Selection - Partial Materialisation 2.22 Data Marting 2.23 ETL 2.24 Data Cleaning 2.25 ELT vs. ETL 2.26 Cloud Data Warehousing Further Reading Exercises Bibliography 3. DATA MINING 3.1 Introduction 3.2 What is Data Mining? 3.3 Data Mining: Definitions 3.4 KDD vs. Data Mining 3.5 DBMS vs. DM 3.6 Other Related Areas 3.7 DM Techniques 3.8 Other Mining Problems 3.9 Issues and Challenges in DM 3.10 DM Application Areas 3.11 DM Applications—Case Studies 3.12 Conclusions Further Reading Exercises Bibliography 4. ASSOCIATION RULES 4.1 Introduction 4.2 What is an Association Rule? 4.3 Methods to Discover Association Rules 4.4 Apriori Algorithm 4.5 Partition Algorithm 4.6 Pincer-Search Algorithm 4.7 Dynamic Itemset Counting Algorithm 4.8 FP-tree Growth Algorithm 4.9 Eclat and dEclat 4.10 Rapid Association Rule Mining (RARM) 4.11 Discussion on Different Algorithms 4.12 Incremental Algorithm 4.13 Border Algorithm 4.14 Generalised Association Rule 4.15 Association Rules with Item Constraints 4.16 Summary Further Reading Exercises Bibliography 5. CLUSTERING TECHNIQUES 5.1 Introduction 5.2 Clustering Paradigms 5.3 Partitioning Algorithms 5.4 k-Medoid Algorithms 5.5 CLARA 5.6 CLARANS 5.7 Hierarchical Clustering 5.8 DBSCAN 5.9 BIRCH 5.10 CURE 5.11 Categorical Clustering Algorithms 5.12 STIRR 5.13 ROCK 5.14 CACTUS 5.15 Conclusions Further Reading Exercises Bibliography 6. DECISION TREES 6.1 Introduction 6.2 What is a Decision Tree? 6.3 Tree Construction Principle 6.4 Best Split 6.5 Splitting Indices 6.6 Splitting Criteria 6.7 Decision Tree Construction Algorithms 6.8 CART 6.9 ID3 6.10 C4.5 6.11 CHAID 6.12 Summary 6.13 Decision Tree Construction with Presorting 6.14 RainForest 6.15 Approximate Methods 6.16 CLOUDS 6.17 BOAT 6.18 Pruning Technique 6.19 Integration of Pruning and Construction 6.20 Summary: An Ideal Algorithm 6.21 Other Topics 6.22 Conclusions Further Reading Exercises Bibliography 7. ROUGH SET THEORY 7.1 Introduction 7.2 Definitions 7.3 Example 7.4 Reduct 7. 5 Propositional Reasoning and PIAP to Compute Reducts 7.6 Types of Reducts 7.7 Rule Extraction 7.8 Decision tree 7.9 Rough Sets and Fuzzy Sets 7.10 Granular Computing Further Reading Exercises Bibliography 8. GENETIC ALGORITHM 8.1 Introduction 8.2 Basic Steps of GA 8. 3 Selection 8.4 Crossover 8.5 Mutation 8.6 Data Mining Using GA 8.7 GA for Rule Discovery 8.8 GA and Decision Tree 8.9 Clustering Using GA Conclusions Further Reading Exercises Bibliography 9. OTHER TECHNIQUES 9.1 Introduction 9.2 What is a Neural Network? 9.3 Learning in NN 9.4 Unsupervised Learning 9.5 Data Mining Using NN: A Case Study 9.6 Support Vector Machines 9.7 Conclusions Further Reading Exercises Bibliography 10. Performance Evaluation - ROC Curve 10.1 Introduction 10.2 Classification Accuracy 10.3 ROC Space 10.4 ROC Curves 10.5 ROC Curves and Class Distribution 10.6 ROC Convex Hull (ROCCH) 10.7 Method to Find the Optimal Threshold Point 10.8 Combining Classifiers 10.9 Area Under the ROC Curve (AUC ) 10.10 Methods to Compute AUC 10.11 Averaging ROC Curves 10.12 R OC for Multi-class Classifiers 10.13 Precision–Recall Graph 10.14 DET Curves 10.15 Cost Curves Further Reading Exercises Bibliography 11. WEB MINING 11.1 Introduction 11.2 Web Mining 11.3 Web Content Mining 11.4 Web Structure Mining 11.5 Web Usage Mining 11.6 Text Mining 11.7 Unstructured Text 11.8 Episode Rule Discovery for Texts 11.9 Hierarchy of Categories 11.10 Text Clustering 11.11 Conclusions Further Reading Exercises Bibliography 12. TEMPORAL AND SPATIAL DATA MINING 12.1 Introduction 12.2 What is Temporal Data Mining? 12.3 Temporal Association Rules 12.4 Sequence Mining 12.5 The GSP Algorithm 12.6 SPADE 12.7 SPIRIT 12.8 WUM 12.9 Episode Discovery 12.10 Event Prediction Problem 12.11 Time-series Analysis 12.12 Spatial Mining 12.13 Spatial Mining Tasks 12.14 Spatial Clustering 12.15 Spatial Trends 12.16 Conclusions Further Reading Exercises Bibliography Index 9789386235053

]]> Place hold on Data mining techniques