Data Mining & Data Warehousing

Learn advanced methodologies in data preprocessing, classification, clustering, and association rules, while gaining a solid understanding of the architecture and design of data warehouses. This book explores the essential concepts, techniques, and tools crucial for extracting valuable insights from vast datasets. Each chapter equips you with practical skills to analyze and manage data effectively, empowering you to turn complex information into actionable insights.

• Data Architecture
• Introduction To Data Mining
• Data-warehouses
• Classification Of Data Mining Systems
• Data Mining Functionalities
• Clustering And Sampling
• Transactional Databases
• Data Cleaning
• Dimensionality Reduction
• Major Issues In Data Mining
• Data Reduction
• Advanced Data And Information Systems And Advanced Applications
• Data Cleaning Process
• Noisy Data
• Data Transformation
• Descriptive Data Summarization
• Data Mining Task Primitives
• Introduction To Data Preprocess
• Performance Issues In Data Mining
• Data Integration And Transformation
• Numerosity Reduction
• Graphic Displays Of Basic Descriptive Data Summaries
• Measuring The Dispersion Of Data
• Data Discretization And Concept Hierarchy Generation
• Concept Hierarchy Generation For Categorical Data
• Integration Of A Data Mining System With A Datawarehouse System
• Star-cubing: Computing Iceberg Cubes Using A Dynamic Star-tree Structure
• Multiway Array Aggregation For Full Cube Computation
• Differences Between Operational Database Systems And Data Warehouses
• Pre-computing Shell Fragments For Fast High-dimensional Olap
• Introduction To Data Warehouses
• Data Warehouse Architecture
• A Multidimensional Data Model
• Data Warehouse Implementation
• On-line Analytical Processing To On-line Analytical Mining
• The Process Of Data Warehouse Design
• Data Warehousing To Data Mining
• A Three-tier Data Warehouse Architecture
• Attribute-oriented Induction For Data Characterization
• Data Warehouse Back-end Tools And Utilities
• Methods For Data Cube Computation
• Attribute-oriented Induction
• Types Of Olap Servers: Rolap Versus Molap Versus Holap
• Mining Class Comparisons: Discriminating Between Different Classes
• Efficient Implementation Of Attribute-oriented Induction
• Complex Aggregation At Multiple Granularity: Multi Feature Cubes
• Driven Exploration Of Data Cubes
• Mining Closed Sequential Patterns
• Prefix Span: Prefix-projected Sequential Pattern Growth
• Frequent Pattern Based Clustering Methods
• Frequent-pattern Mining In Data Streams
• Vector Objects
• Stream: A K-medians-based Stream Clustering Algorithm
• User-constrained Cluster Analysis
• Chameleon: A Hierarchical Clustering Algorithm Using Dynamic Modeling
• Clique: A Dimension-growth Subspace Clustering Method
• Spade: An A Priori-based Vertical Data Format Sequential Pattern Mining Algorithm
• Ratio-scaled Variables
• Sting: Statistical Information Grid
• Rock: A Hierarchical Clustering Algorithm For Categorical Attributes
• Representative Object-based Technique
• Periodicity Analysis For Time-related Sequence Data
• Similarity-search-methods
• Density-based Methods
• Constraint-based Cluster Analysis
• Denclue: Clustering Based On Density Distribution Functions
• Distance-based Outlier Detection
• Stream Olap And Stream Data Cubes
• Model-based Clustering Methods
• Mining Sequence Patterns In Biological Data
• Ordinal Variables
• Scalable Methods For Mining Sequential Patterns
• Grid-based Methods
• Mining Sequence Patterns In Transactional Databases
• Constraint-based Mining Of Sequential Patterns
• Baum-welch Algorithm
• Very Fast Decision Tree (Vfdt) And Concept-adapting Very Fast Decision Tree (Cvfdt)
• Clustering Evolving Data Streams
• Methodologies For Stream Data Processing And Stream Data Systems
• Clustering With Obstacle Objects
• Statistical Distribution-based Outlier Detection
• Categorical Variables
• Outlier Analysis
• Challenges In Clustering
• Clustering High-dimensional Data
• Mining Time-series Data
• Interval-scaled Variables
• Binary Variables
• Viterbi Algorithm
• Types Of Data In Cluster Analysis
• Classical Partitioning Methods: K-means And K-medoids
• Neural Network Approach
• Mining Data Stream
• Semi-supervised Cluster Analysis
• Proclus: A Dimension-reduction Subspace Clustering Method
• Forward Algorithm
• Introduction To Cluster
• Deviation-based Outlier Detection
• A Categorization Of Major Clustering Methods
• Hidden Markov Model For Biological Sequence Analysis
• Conceptual Clustering
• Hierarchical Methods
• Similarity Search In Time-series Analysis
• Classification Of Dynamic Data Streams
• Training Bayesian Belief Networks
• Wave Cluster: Clustering Using Wavelet Transformation
• Estimating Confidence Intervals
• Rule Induction Using A Sequential Covering Algorithm
• The Apriori Algorithm
• Frequent Patterns
• Support Vector Machines
• Roc Curves
• Mining Customer Networks For Viral Marketing
• Genetic Algorithms
• Generalization Of Object Identifiers And Class/subclass Hierarchies
• Decision Tree Induction
• Mining Frequent Itemsets Using Vertical Data Format
• Data Mining System Products And Research Prototypes
• Bagging
• Introduction To Classification And Prediction
• Classification And Prediction Analysis Of Multimedia Data
• Mining Spatial Association And Co-location Patterns
• Naive Bayesian Classification
• Data Mining In Other Scientific Applications
• Mining Multilevel Association Rules
• Multi-relational Classification Using Tuple Id Propagation
• Bayesian Classification
• Graph Indexing With Discriminative Frequent Substructures
• Association Mining To Correlation Analysis
• Bayesian Belief Networks
• Introduction To Data Mining Applications
• Adaboost, A Boosting Algorithm
• Multidimensional Analysis And Descriptive Mining Of Complex Data Objects
• Classification By Decision Tree Induction
• Evaluating The Accuracy Of A Classifier Or Predictor
• Generalization Of Structured Data
• Mining Variant And Constrained Substructure Patterns
• Comparing Classification And Prediction Methods
• Data Mining For The Telecommunication Industry
• Mining Multimedia Data On The Web
• Introduction To Back Propagation
• Document Clustering Analysis
• Efficient And Scalable Frequently Itemset Mining Methods
• Constraint-based Association Mining
• Tree Pruning
• Community Mining From Multi Relational Networks
• Mining Multidimensional Association Rules
• Rule Extraction From A Decision Tree
• Mining Associations In Multimedia Data
• Ubiquitous And Invisible Data Mining
• Rule Pruning
• Data Mining, Privacy, And Data Security
• Mining Quantitative Association Rules
• Tuple Id Propagation
• Preparing The Data For Classification And Prediction
• Spatial Data Cube Construction And Spatial Olap
• Defining A Network Topology
• Dimensionality Reduction For Text
• Associative Classification: Classification By Association Rule Analysis
• Scalability And Decision Tree Induction
• Aggregation And Approximation In Spatial And Multimedia Data Generalization
• Mining The Web's Link Structures To Identify Authoritative Web Pages
• A Multilayer Feed-forward Neural Network
• Using If-then Rules For Classification
• Mining The World Wide Web
• Data Mining And Collaborative Filtering
• Mining The Web Page Layout Structure
• Social Network Analysis
• Introduction To Graph Mining
• Text Mining Approaches
• Classification And Cluster Analysis Using Graph Patterns
• Pattern-growth Approach
• Statistical Data Mining
• Characteristics Of Social Networks
• Text Retrieval Methods
• Trends In Data Mining
• Text Mining
• Data Mining For Intrusion Detection
• General Theory Of Pipeline
• Ilp Approach To Multi-relational Classification
• Methods For Mining Frequent Sub Graphs
• Text Indexing And Query Processing Techniques
• Mining Newsgroups Using Networks
• Link Mining: Tasks And Challenges
• Multi-relational Data Mining
• Text Data Analysis And Information Retrieval
• Link Prediction: What Edges Will Be Added To The Network
• Multimedia Data Mining
• Visual And Audio Data Mining
• Spatial Clustering Methods
• Web Usage Mining
• Multi-relational Clustering With User Guidance
• Audio And Video Data Mining
• Examples Of Commercial Data Mining Systems
• Spatial Data Mining
• Substructure Similarity Search In Graph Databases
• Theoretical Foundations Of Data Mining
• Data Mining For Biological Data Analysis