CSC603: Data Warehousing & Mining Syllabus for CS 6th Sem 2018 Pattern Mumbai University

Data Warehousing & Mining detailed syllabus scheme for Computer Engineering (CS), 2018 regulation has been taken from the University of Mumbai official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.

For all other Mumbai University Computer Engineering 6th Sem Syllabus 2018 Pattern, do visit CS 6th Sem 2018 Pattern Scheme. The detailed syllabus scheme for data warehousing & mining is as follows.

Data Warehousing & Mining Syllabus for Computer Engineering TE 6th Sem 2018 Pattern Mumbai University

Course Objectives:

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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Course Outcomes:

On successful completion of course learner will be able to:

1. Understand Data Warehouse fundamentals, Data Mining Principles
2. Design data warehouse with dimensional modelling and apply OLAP operations.
3. Identify appropriate data mining algorithms to solve real world problems
4. Compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining
5. Describe complex data types with respect to spatial and web mining.
6. Benefit the user experiences towards research and innovation.

Prerequisites:

Basic database concepts, Concepts of algorithm design and analysis.

Module 1

Introduction to Data Warehouse and Dimensional modelling: Introduction to Strategic Information, 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. 8

Module 2

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Module 3

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, Reduction: Attribute subset selection, Histograms, Clustering and Sampling, Data Transformation & Data Discretization: Normalization, Binning, Concept hierarchy generation, Concept Description: Attribute oriented Induction for Data Characterization. 10

Module 4

Classification, Prediction and Clustering: Basic Concepts, Decision Tree using Information Gain, Induction: Attribute Selection Measures, Tree pruning, Bayesian Classification: Naive Bayes, Classifier Rule – Based Classification: Using IF-THEN 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 (UMeans, UMedoids), Hierarchical Methods(Agglomerative, Divisive. 12

Module 5

Mining Frequent Patterns and Association Rules: 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 8

Module 6

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.
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Text Books:

1. PaulrajPonniah, Data Warehousing: Fundamentals for IT Professionals, Wiley India.
2. Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 3rd edition.
3. ReemaTheraja Data warehousing, Oxford University Press.
4. M.H. Dunham, “Data Mining Introductory and Advanced Topics”, Pearson Education.

Reference Books:

1. Ian H. Witten, Eibe Frank and Mark A. Hall ” Data Mining “, 3rd Edition Morgan kaufmann publisher.
2. Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining”, Person Publisher.
3. R. Chattamvelli, “Data Mining Methods” 2nd Edition NarosaPublishing House.

Internal Assessment: Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed. Duration of each test shall be one hour. End Semester Theory Examination:

1. Question paper will comprise of 6 questions, each carrying 20 marks.
2. The students need to solve total 4 questions.
3. Question No.1 will be compulsory and based on entire syllabus.
4. Remaining question (Q.2 to Q.6) will be selected from all the modules.

For detail syllabus of all other subjects of Computer Engineering (CS) 6th Sem 2018 regulation, visit CS 6th Sem Subjects syllabus for 2018 regulation.