Data Warehousing

Data Warehouse: Data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process. [Bill W. H. Inmon ]. 

Data warehousing : Data warehousing is a collection of decision support technologies, aimed at enabling the knowledge worker to make better decisions.   Requirements of a Data Warehouse system Efficient cube computation,  Better access methods, Efficient query processing. In this tutorial, we will learn about the following concepts.

DATA WAREHOUSING

Data Warehouse, Operational Database Systems versus Data Warehouses, A Multi tired Architecture, A Multidimensional Data Model, Stars, Snowflakes and Fact Constellations: Schemas, Role of Concept hierarchies, Measures, OLAP Operations, From online Analytical processing to Multidimensional Data Mining, Indexing OLAP Data.

 

DATA MINING 

Introduction to Data Mining, kinds of data, kinds of patterns, major issues in Data Mining

DATA PREPROCESSING

Data Pre-processing, Data Cleaning, Data Integration , Data Reduction, Data Transformation and

Discretization.

ASSOCIATIONS 

Basic Concepts , Frequent itemset Mining Methods, pattern evaluation methods- From Association Mining to Correlation Analysis.

CLASSIFICATION

Classification, Decision Tree Introduction, Bayesian Classification Methods, Rule Based Classification, Prediction: Linear Regression.

CLUSTER ANALYSIS

Cluster Analysis: Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods -k-Means and K-Medoids, Hierarchical methods-Agglomerative and divisive method, Density-Based Method-DBSCAN, Grid-Based Method-STING, Outlier Analysis.

MiningComplex Data Types:

Mining sequence data, Mining other kinds of data: Spatial, Text, Multimedia and Web data, Data Mining Trends.