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DATA MINING

Data warehouse: 

Introduction to Data warehouse, Difference between operational database systems and Data warehouses. Differences between operational databases systems and data warehouses. 

Multidimensional data model: From Tables and spreadsheets to Data Cubes, stars, Snowflakes, and Fact Constellations schemas for Multidimensional databases. Examples for defining star, snowflake and fact constellation schemas. 

Data Warehouse Architecture: Steps for the design and construction of data warehouses. A three-tier data warehouse architecture. From Data warehousing to data mining: Data warehouse usage, from on-line analytical processing to online analytical mining.

Data Mining Introduction: Data mining-on what kind of data, Relational databases, data warehouses, transactional databases, advanced database systems and advanced database applications. Data mining functionalities, classification of data mining systems, Major issues in data mining. 

Data Pre-processing: Data cleaning: Missing values, Noisy data, inconsistent Data, Data Integration and Transformation: Data Integration, Data transformation Data Reduction: Data cube aggregation, dimensionality reduction, data compression, Numerosity reduction.

Association Rule mining in Large Databases: Association rule mining, mining single dimensional Boolean association rules from transaction databases, Mining multilevel association rules from transaction databases. Mining multidimensional association rules from relational databases. From association mining to correlation analysis. Constraint based association mining.

Classification and Prediction: Issues regarding classification and prediction, Classification by decision tree induction, Bayesian classification, Classification by back propagation, Prediction, classification accuracy.

Cluster Analysis: Types of data in cluster analysis, a categorization of major clustering methods, Partition based methods: K-means, K-medoids. Hierarchical methods: BIRCH, CURE Density-based methods: DBSCAN.

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