1Chapter 1. Introduction
435.2 Mining Time Series Data
21.1 Relational Database
445.3 Mining Sequence patterns in a transactional
31.2 Transaction database
45database
41.3 Data Mining Functionalities
465.4 Mining Sequence Patterns in Biological Data
51.4 Classification of data mining systems
475.5 Questionnaire
61.5 Integrating a Data Mining System with a
48Chapter 6. Graph Mining,Social Network Analysis, andMulti-Relational Data Mining
7DB/DW System
496.1 Graph Mining
81.5 The main problems in data mining
506.2 Social Network Analysis
91.6 Questionnaire
516.3 Multi-Relational Data Mining
10Chapter 2. Data Processing
526.4 Questionnaire
112.1 Introduction
53Chapter 7. Applications and Trends in Data Mining
122.2 Descriptive data summarization
547.1 Data Mining Applications
132.3 Data Cleaning
557.2 Data Mining System Products
142.4 Data integration and transformation
567.3 Data Mining system Products and Research
152.5 Data Reduction
57Prototypes
162.6 Data discretization and concept hierarchy
587.3 Additional themes on data mining
17generation
597.4 Statistical Data Mining
182.7 Questionnaire
607.5 Visual Data Mining
19Chapter 3. Data Warehouse and OLAP Technology
617.6 Audio Data Mining
203.1 Introduction
627.7 Data Mining and Collaborative Filtering
213.2 A multidimensional data model
637.8 Social impacts on data mining
223.3 Data Warehouse Architecture
647.9 Trends in data mining
233.4 Data Storage System Structure Features
657.10 Wonders Data Mining
243.5 Types of data storage architecture
667.11 Questionnaire
253.6 Data warehouse implementation
67Chapter 8. Frequent Pattern Mining
263.7 Data warehouse to data mining
688.1 Introduction
273.8 Questionnaire
698.2 Frequent Pattern Mining Algorithms
28Chapter 4. Cluster Analysis
708.3 Scalability Issues in Frequent Pattern
294.1 Introduction
71Mining
304.2 Clustering in Data Mining
728.4 Frequent pattern mining with advanced
314.3 Types of data in cluster analysis
73data types
324.4 A characterization of major clustering
748.5 Frequent pattern mining applications
33methods
758.6 Questionnaire
344.5 Hierarchical Methods
76Chapter 9. Trends and Research Frontiers in Data Mining
354.6 Density-Based Methods
779.1 Mining Complex Types of Data
364.7 Grid-based method
789.2 Other Methodologies of Data Mining
374.8 Model-Based Methods
799.3 Data Mining Applications
384.9 Clustering high dimensional data
80 9.4 Data Mining and Society
394.10 Constraint-based cluster analysis
819.5 Trends in Data Mining
404.11 Questionnaire
829.6 Questionnaire
41Chapter 5. Mining Stream, Time Series, and Sequence Data
83Glossary
425.1 Mining Data Streams
