DATA MINING AND WAREHOUSING by jwYdo9ua

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									DATA MINING AND WAREHOUSING

MODULE: I

Unit 1- Fundamentals of Data Mining: Defnition, Motivation, what
kinds of data? Data Mining Functionalities

Unit 2 - Classification of Data Mining Systems, Major Issues in Data
Mining.

Unit 3 - Data Warehouse and OLAP Technology for Data Mining:
Definition, Multidimensional datamodels, Data Warehouse Architecture

Unit 4 - Data Warehouse Implementation, Further development of
data cube technology, From Data Warehousing to Data Mining.

MODULE: II

Unit 1 - Data Preprocessing: Need for preprocess the data, Data
Cleaning, Data Integration, Data Transformation

Unit 2 - Data Reduction, Discretization and Concept of Hierarchy
Generation.

Unit 3 - Data Mining Primitives, Languages and system Architectures:
Data Mining Primitives, Architectures of Data Mining System.

Unit 4 - A Data Mining Query Language, Designing Graphical User
Interfaces Based on a Data Mining Query Language

MODULE: III

Unit 1 - Concept Description-Characterization: Definition, Data
Generalization and Summarization-Based Characterization, Analytical
Characterization

Unit 2 - Concept Description-Comparison: Mining Class Comparisons,
Mining Descriptive Statistical Measures in Large Databases

Unit 3 - Mining Association Rules in Large Databases: Association Rule
Mining, Mining Single–Dimensional Boolean Association Rules form
Transactional Databases
Unit 4 - Mining Multilevel Association Rules form Transaction
Databases, Mining Multidimensional Association Rules from Relational
Databases and Data Warehouses.

MODULE: IV

Unit 1 - Classification and Prediction: Definition, Issues Regarding
Classification and Prediction

Unit 2 - Classification by Decision Tree Induction,           Bayesian
Classification, Classification by Back propagation

Unit 3 - Classification Based on Concepts form Association Rule
Mining, Other Classification Methods – Prediction, Classifier Accuracy.

MODULE: V

Unit 1 – Cluster Analysis: Definition, Types of data, Clustering
methods

Unit 2 - Partitioning methods, Hierarchical methods, Density based
methods

Unit 3 – Grid based methods, Model based clustering methods, Outlier
analysis

Module: VI

Unit 1 - Mining Complex Types of Data: Multidimensional Analysis
and Descriptive Mining of Complex Data Objects, Mining Spatial
Databases

Unit 2 - Mining Multimedia Databases, Mining Time – Series and
Sequence Data , Mining Text Databases, Mining the World
Wide Web.

Unit 3 - Applications and Trends in Data Mining: Data Mining
Applications, Data Mining /system Products and Research Prototypes

Unit 4 - Additional Themes on Data Mining, Social Impacts of /data
Mining, Trends in /data Mining.

								
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