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ST. ANN’S COLLEGE OF ENGINEERING &TECHNOLOGY: CHIRALA DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING LESSON PLAN Subject: Data Warehousing And Data Mining Name : CH.VEERABABU Unit No. I II Academic Year: 2015-16 Year & Sem/Section: IV-I-SEM 'A' Sub Topic Names Introduction to DWDM:What is Data Mining,Motivating Challenges,Origins of Data Mining,Data Mining Tasks Types of Data-Attributes and Measurements,Types of Data sets, Data Quality Data Preprocessing,Measures of Similarity and Dissimilarity Basics,Similarity and Dissimilarity between simple Attributes ,Dissimilarities between Data Objects, Examples of Proximity measures,Similarity Measures for Binary Data,,Jaccard Coeeficient, Cosine Similarity, No. of classes required 06 10 Extended Jaccard Coeeficient, correlation, Exploring data: dataset, summary statistics III Basic Concepts of Data warehouse, Data warehousing Modelling:Data Cube and OLAP,Data warehouse implementation:Efficient Data Cube Computation Partial Materilization,Indexing OLAP Data Efficient Processing of OLAP Queries 05 Basic Concepts of Classification, General Approach to solving a clasification Problem,Working of Decission Tree induction: working of desition tree,Building a Decission Tree,Methods for Expressing Attribute Test Conditions,Measures For selecting the best split, Algorithm for Decission Tree IV V VI 09 Induction,Model Over fitting:Due to presence of noise, Due to lack of representation samples,Evaluating the performance of Classifier: Holdout Method, Random sub smapling,Cross-validation, Bootstrap Classification Alternative Techniques: Bayesian classifier: Bayes Theorem,Using Bayes Theorem for Classification, Navie Bayes Classifier, Bayes Error Rate,Model Representation, Model Building. 06 Association analysis:problem definition,frequent item set generation-apriori principle, frequent item set generation in the apriori algorithm,candidate generation and pruning,support counting,rule generation,compact representation of frequent item sets,fp-growth algorithms 07 VII VIII Overview Types of Clustering, Basic K-Means, K-Means additional Issues, Bi-secting K-Means K-means and different types of clusters, Strengths and weaknesses, K-Means as an optimization problem 07 Agglomarative Hierarchical Clustering, Basic Agglomarative Hierarchical Clustering Algorithm, Specific Techniques, DBSCAN, Traditional Density: Center Based Approach, Strengths and weaknesses 06 TOTAL 56 TEXT BOOKS: 1. Introduction to Data Mining : Pang-Ning tan, Michael Steinbach,Vip Kumar, Pearson 2. Data Mining, Concepts and Techniques,3/e Jiawei HAN ,Micheline KAMBER, Elsevier REFERENCE BOOKS: 1. 2. 3. 4. Introduciton to Data Mining with Case studies 2nd ed: Gk Gupta; PH Data Mining: Introductory and Advanced Topics: Dunham , Sridhar Pearson. Data Warehousing .Data Mining & OLAP, Alex Berson, Stephen j Smith, TMH Data Mining Theory and Practice, Somnan, Diwakar, Ajay PHI,2006 FACULTY MEMBER HEAD OF THE DEPARTMENT