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Data Warehousing and Mining (Introduction to Data Mining, Data Exploration…
Data Warehousing and Mining
Introduction to Data Warehouse and Dimensional modelling
Need for Strategic Information, Features of Data Warehouse,
Data warehouses versus Data Marts, Top-down versus Bottom-up approach. Data
warehouse architecture, metadata, E-R modelling versus Dimensional Modelling,
Information Package Diagram, STAR schema, STAR schema keys, Snowflake
Schema, Fact Constellation Schema, Factless Fact tables, Update to the dimension
tables, Aggregate fact tables
ETL Process and OLAP
Major steps in ETL process, Data extraction:
Techniques, Data transformation: Basic tasks, Major transformation types, Data
Loading: Applying Data, OLTP Vs OLAP, OLAP definition, Dimensional
Analysis, Hypercubes, OLAP operations: Drill down, Roll up, Slice, Dice and
Rotation, OLAP models : MOLAP, ROLAP.
Introduction to Data Mining, Data Exploration and Preprocessing
Data Mining Task Primitives, Architecture, Techniques, KDD process
, Issues in Data Mining, Applications of Data Mining, Data Exploration :Types of Attributes,
Statistical Description of Data, Data Visualization
Data Preprocessing
Cleaning,
Integration
Data Discretization: Normalization, Binning,
Concept hierarchy generation, Concept Description: Attribute oriented Induction
for Data Characterization
Sampling, Data Transformation
Reduction
Histograms
Clustering
Attribute subset selection
: Market Basket Analysis,
Frequent Item sets, Closed Item sets, and Association Rule, Frequent Pattern
Mining, Efficient and Scalable Frequent Item set Mining Methods: Apriori
Algorithm, Association Rule Generation, Improving the Efficiency of Apriori, FP
growth, Mining frequent Itemsets using Vertical Data Format, Introduction to
Mining Multilevel Association Rules and Multidimensional Association Rules
Spatial Data, Spatial Vs. Classical Data Mining, Spatial
Data Structures, Mining Spatial Association and Co-location Patterns, Spatial
Clustering Techniques: CLARANS Extension, Web Mining: Web Content Mining,
Web Structure Mining, Web Usage mining, Applications of Web Mining
Basic Concepts, Decision Tree using
Information Gain, Induction: Attribute Selection Measures, Tree pruning, Bayesian
Classification: Naive Bayes, Classifier Rule - Based Classification: Using IFTHEN Rules for classification, Prediction: Simple linear regression, Multiple linear
regression Model Evaluation & Selection: Accuracy and Error measures, Holdout,
Random Sampling, Cross Validation, Bootstrap, Clustering: Distance Measures,
Partitioning Methods (k-Means, k-Medoids), Hierarchical Methods(Agglomerative,
Divisive)