17 cs002

17 cs002

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  17CS002 - DATAWAREHOUSING& DATAMINING Course Description and Objectives: This course is aimed at offering data and information management, information retrieval, and knowledge discovery in modern organizations. Case studies of those organizations using technologies to support business intelligence gathering and decision making are explored. This course is designed to understand the issues relating to the feasibility, usefulness, effectiveness, and scalability of techniques used for the discovery of patterns hidden in large data sets and also characterizes the kind of patterns that can be discovered by association rule mining, classification and clustering Course Outcomes: Students are able to  Learn the basic concepts of Database Technology Evaluation steps and also understood the need of data mining and its functionalities  Explore the efficient and effective maintenance of Data Warehouses.  Apply the data mining functionalities like Clustering, Classification, Association Analysis to real world data.  Discover interesting patterns and association rules from huge volume of data used to do classifications and predictions.  Gain knowledge on developing areas like Web Mining, Text Mining, and Spatial Mining. Skills:  Design and development of schema models for a data warehouse  Extraction of hidden interesting association rules  Implementation of various classification and clustering algorithms  Extraction of knowledge from text databases UNIT- I Introduction: Why Data Mining, What is Data Mining, Kinds of Data, Kinds of Patterns, and Technologies used, Kinds of applications adopted, Major issues in Data Mining. Data Warehousing and Online Analytical Processing: Basic Concepts, Data Warehouse Modeling, Data Warehouse Design and Usage, Data Warehouse Implementation, Data Generalization by Attribute-Oriented Induction UNIT– II About Data: Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Measuring Data Similarity and Dissimilarity Data Preprocessing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization UNIT- III Data Cube Technology: Preliminary Concepts, Data Cube Computation Methods, Processing Advanced Kinds of Queries by Exploring Cube Technology, Multidimensional Data Analysis in Cube Space
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  Mining Frequent Patterns,Associations, and Correlations: Basic Concepts and Methods: Basic Concepts, Frequent Itemset Mining Methods, Which Patterns Are Interesting?—Pattern Evaluation Methods Advanced Pattern Mining: Pattern Mining in Multilevel, Multidimensional Space, Constraint-Based Frequent Pattern Mining. UNIT- IV Classification: Basic Concepts, Decision Tree Induction, Bayes Classification Methods, Rule-Based Classification, Model Evaluation and Selection, Techniques to Improve Classification Accuracy Advanced Classification: Bayesian Belief Networks, Classification by Back propagation, Support Vector Machines, Classification Using Frequent Patterns, Lazy Learners, Other Classification Methods UNIT- V Cluster Analysis: Cluster Analysis, Partitioning Methods, Hierarchical Methods, Density- Based Methods, Grid-Based Methods, Evaluation of Clustering Advanced Cluster Analysis: Probabilistic Model-Based Clustering, Clustering High- Dimensional Data.