Learning Solution – Online Education

Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised) MULTIMEDIA SYSTEMS

Elective – I: MULTIMEDIA SYSTEMS
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	–
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		–	–
ORAL		–	25
TERM WORK		–	25
Prerequisite: Computer Graphics
Objective: Students will be able to understand the relevance and underlining infrastructure of multimedia system. The purpose of the course for the students is to apply contemporary theories of multimedia learning to the development of multimedia products. Analyze instructional and informational media (audio/ visual materials, web based materials, games and simulations etc).

1. Multimedia Basics, Multimedia Authoring and Tools: What is Multimedia?, Multimedia and Hypermedia, World Wide Web, Overview of Multimedia Software Tools, Further Exploration, Multimedia Authoring, Some Useful Editing and Authoring Tools, VRML.

2. Graphics and Image Data Representation: Graphics/Image Data Types 60, Popular File Formats.

3. Concepts in Video and Digital Audio: Color Science, Color Models in Images, Color Models in Video. Types of Video Signals, Analog Video, Digital Video, Digitization of Sound, MIDI: Musical Instrument Digital Interface, Quantization and Transmission of Audio.

4. Lossless & Lossy Compression Algorithms: Introduction, Basics of Information Theory, Run-Length Coding, Variable-Length Coding, Dictionary-Based Coding, Arithmetic Coding, Lossless Image Compression. Distortion Measures, The Rate-Distortion Theory, Quantization, Transform Coding, Wavelet-Based Coding, Wavelet Packets, Embedded Zerotree of Wavelet Coefficients, Set Partitioning in Hierarchical Trees (SPIHT).

5. Image Compression Standards: The JPEG Standard, The JPEG2000 Standard, The JPEG-LS Standard, Bilevel Image Compression Standards.

6. Basic Video Compression Techniques: Introduction to Video Compression, Video Compression Based on Motion Compensation, Search for Motion Vectors, H.261, H.263 303.

7. MPEG Video Coding: Overview, MPEG-1, MPEG-2, Object-Based Visual Coding in MPEG-4, Synthetic Object Coding in MPEG, MPEG-4 Object types, Profiles and Levels, MPEG-4 Part10/H.264, MPEG-7.

8. Basic Audio & MPEG Audio Compression Techniques: ADPCM in Speech Coding, G.726 ADPCM, Vocoders, Psychoacoustics, MPEG Audio, Other Commercial Audio Codecs, future: MPEG-7 and MPEG-2.

9. Computer and Multimedia Networks: Basics of Computer and Multimedia Networks, Multiplexing Technologies, LAN and WAN, Access Networks, Common Peripheral Interfaces.

10. Multimedia Network Communications and Applications: Quality of Multimedia Data Transmission, Multimedia over IP, Multimedia over ATM Networks, Transport of MPEG-4, Media-on-Demand (MOD), Multimedia over Wireless Networks.

11. Content-Based Retrieval in Digital Libraries: How Should We Retrieve Images?, C-BIRD— A Case Study, Synopsis of Current Image Search Systems, Relevance Feedback. Quantifying Results, Querying on Videos, Querying on Other Formats, Outlook for Content-Based Retrieval.

12. Image Databases: Raw Images, Compress Image Presentations, Image Processing Segmentation, Similarity- Based Retrieval, Alternating Image DB Paradigms, Representing Image DBs with Relations and R Trees, Retrieving Images by Special Layout, Implementations, Selected Commercial Systems.

13. Text/Document Databases: Precision and Recall, Stop Lists, Word Stems and Frequency tables, Latent Semantic Indexing, TV-Trees, Other Retrieval Techniques, Selected Commercial Systems.

14. Video & Audio Databases: Organizing content of a Single video, Querying content of Video Libraries, Video Segmentation, Video Standard and Selected Commercial Systems. A general Model of Audio Data, Capturing Audio Content through Discrete Transformation, Indexing Audio Data and Selected Commercial Systems.

15. Multimedia Databases: Design and Architecture of a Multimedia Database, Organizing Multimedia Data based on the Principal of Uniformity, Media Abstractions, Query Languages for Retrieving Multimedia Data , Indexing SMDSs with Enhanced Inverted Indices, Query Relaxation/ Expansion, Conclusions and Selected Commercial Systems.

Text Books:

1. Ze-Nian Li and M. S. Drew, “Fundamental of Multimedia”, Pearson Education.

2. V. S. Subrahmanian, “Principles of Multimedia Database Systems”, Morgan Kaufmann Punlication.

Reference Books:

1. K. R. Rao, Zoran S. Bojkovic, D. A. Milovanovic, “Introduction to Multimedia Communications”, Wiley.

2. R. Steinmetz and K. Nahrstedt “Multimedia: Computing, Communication & Applications, Pearson Education.

3. Buford, “Multimedia Systems”, Pearson Education.

4. C. T. Bhunia, “Multimedia and multimedia Communications”, New Age International Publishers.

5. Prabhat K. Andheigh, Kiran Thakrar, “Multimedia Systems design’, PHI.

6. Koegel Buford, “Multimedia Systems”, Pearson Eduaction.

7. J. D. Gibson, ‘Multimedia Communications: Directions and Innovations’, Academic Press, Hard-court India.

8. Free Halshal, ‘Multimedia Communications’, PEA.

Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment List

1. Study of multimedia I/O devices.

2. Calculator for blind

3. Media player application

4. Design advertisement using flash/macromedia

5. Design a web application using dream viewer and fireworks

6. Create multimedia database for student ID card preparation

7. Study and use of different MPEG file formats.

8. Construction of website using pictures, videos, audio etc with proper layout.

9. Implementation Huffman algorithm for six character long string.

10. Edit the movie clip using adobe premiere.

11. Record a speech and perform compression and decompression.

12. Design a game/application in flash.

13. Convert BMP file to JPG file using any programming language.

Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised) SOFTWARE TESTING & QUALITY ASSURANCE

SOFTWARE TESTING &QUALITY ASSURANCE
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	–
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		–
ORAL		–	25
TERM WORK		–	25
Prerequisite: Software Engineering
Objective: This course equips the students with a solid understanding of: • Practices that support the production of quality software • Software testing techniques • Life-cycle models for requirements, defects, test cases, and test results • Process models for units, integration, system, and acceptance testing • Quality Models

1. Introduction: Software Quality, Role of testing, verification and validation, objectives and issues of testing, Testing activities and levels, Sources of Information for Test Case Selection, White-Box and Black-Box Testing , Test Planning and Design, Monitoring and Measuring Test Execution, Test Tools and Automation, Test Team Organization and Management .

2. Unit Testing: Concept of Unit Testing , Static Unit Testing , Defect Prevention , 3.4 Dynamic Unit Testing , Mutation Testing , Debugging , Unit Testing in eXtreme Programming

3. Control Flow Testing: Outline of Control Flow Testing, Control Flow Graph, Paths in a Control Flow Graph, Path Selection Criteria, All-Path Coverage Criterion , Statement Coverage Criterion, Branch Coverage Criterion, Predicate Coverage Criterion, Generating Test Input, Examples of Test Data Selection.

4. Data Flow Testing: Data Flow Anomaly,. Overview of Dynamic Data Flow Testing, Data Flow Graph, Data Flow Terms, Data Flow Testing Criteria, Comparison of Data Flow Test Selection Criteria, Feasible Paths and Test Selection Criteria, Comparison of Testing Techniques.

5. System Integration Testing: Concept of Integration Testing, Different Types of Interfaces and Interface Errors, Granularity of System Integration Testing, System Integration Techniques, Software and Hardware Integration, Test Plan for System Integration, Off-the-Shelf Component Integration, Off-the-Shelf Component Testing, Built-in Testing

6. System Test Categories: Basic Tests, Functionality Tests, Robustness Tests, Interoperability Tests, Performance Tests, Scalability Tests, Stress Tests, Load and Stability Tests, Reliability Tests, Regression Tests, Documentation Tests.

7. Functional Testing: Equivalence Class Partitioning, Boundary Value Analysis, Decision Tables, Random Testing, Error Guessing, Category Partition.

8. System Test Design: Test Design Factors, Requirement Identification, Characteristics of Testable Requirements, Test Design Preparedness Metrics, Test Case Design Effectiveness

9. System Test Planning And Automation: Structure of a System Test Plan, Introduction and Feature Description, Assumptions, Test Approach, Test Suite Structure, Test Environment, Test Execution Strategy, Test Effort Estimation, Scheduling and Test Milestones, System Test Automation, Evaluation and Selection of Test Automation Tools, Test Selection Guidelines for Automation, Characteristics of Automated Test Cases, Structure of an Automated Test Case, Test Automation Infrastructure

10. System Test Execution: Preparedness to Start System Testing, Metrics for Tracking System Test, Metrics for Monitoring Test Execution, Beta Testing, First Customer Shipment, System Test Report, Product Sustaining, Measuring Test Effectiveness.

11. Acceptance Testing: Types of Acceptance Testing, Acceptance Criteria, Selection of Acceptance Criteria, Acceptance Test Plan, Acceptance Test Execution, Acceptance Test Report, Acceptance Testing in eXtreme Programming.

12. Software Quality: Five Views of Software Quality, McCall’s Quality Factors and Criteria, Quality Factors Quality Criteria, Relationship between Quality Factors and Criteria, Quality Metrics, ISO 9126 Quality Characteristics, ISO 9000:2000 Software Quality Standard ISO 9000:2000 Fundamentals, ISO 9001:2000 Requirements

Text Book

1. “Software Testing and Quality Assurance: Theory and Practice”, Sagar Naik, University of Waterloo, Piyu Tripathy, Wiley , 2008

References:

1. “Effective methods for Software Testing “William Perry, Wiley.

2. “Software Testing – A Craftsman’s Approach”, Paul C. Jorgensen, CRC Press, 1995.

3. “The Art of Creative Destruction”, Rajnikant Puranik, SPD.

4. “Software Testing”, Srinivasan Desikan and Gopalaswamy Ramesh – Pearson Education 2006.

5. “Introducing to Software Testing”, Louis Tamres, Addison Wesley Publications, First Edition.

6. “Software Testing”, Ron Patton, SAMS Techmedia Indian Edition, Pearson Education 2001.

7. “The Art of Software Testing”, Glenford J. Myers, John Wiley & Sons, 1979.

8. “Testing Object-Oriented Systems: Models Patterns and Tools”, Robert V. Binder, Addison Wesley, 2000.

9. “Software Testing Techniques”, Boris Beizer, 2nd Edition, Van Nostrand Reinhold, 1990.

10. “Software Quality Assurance”, Daniel Galin, Pearson Education.

Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test.

Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work.

Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised) SIMULATION AND MODELING

SIMULATION AND MODELING
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	–
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		–	25
ORAL		–	–
TERM WORK		–	25
Prerequisite: Probability and Statistics
Objective: The objective of this course is to teach students methods for modeling of systems using discrete event simulation. Emphasis of the course will be on modeling and on the use of simulation software. The students are expected to understand the importance of simulation in IT sector, manufacturing, telecommunication, and service industries etc. By the end of the course students will be able to formulate simulation model for a given problem, implement the model in software and perform simulation analysis of the system.

1. Introduction to Simulation and Modeling: Simulation – introduction, appropriate and not appropriate, advantages and disadvantage, application areas, history of simulation software, an evaluation and selection technique for simulation software, general – purpose simulation packages. System and system environment, components of system, type of systems, model of a system, types of models and steps in simulation study.

2. Manual Simulation of Systems: Simulation of Queuing Systems such as single channel and multi channel queue, lead time demand, inventory system, reliability problem, time-shared computer model, job-shop model.

3. Discrete Event Formalisms: Concepts of discrete event simulation, model components, a discrete event system simulation, simulation world views or formalisms, simulation of single channel queue, multi channel queue, inventory system and dump truck problem using event scheduling approach.

4. Statistical Models in Simulation: Overview of probability and statistics, useful statistical model, discrete distribution, continuous distribution, empirical distribution and Poisson process.

5. Queueing Models: Characteristics of queueing systems, queueing notations, long run measures of performance of queueing systems, Steady state behavior of Markovian models (M/G/1, M/M/1, M/M/c) overview of finite capacity and finite calling population models, Network of Queues.

6. Random Number Generation: Properties of random numbers, generation of true and pseudo random numbers, techniques for generating random numbers, hypothesis testing, various tests for uniformity (Kolmogorov-Smirnov and chi-Square) and independence (runs, autocorrelation, gap, poker).

7. Random Variate Generation: Introduction, different techniques to generate random variate:- inverse transform technique, direct transformation technique, convolution method and acceptance rejection techniques.

8. Input Modeling: Introduction, steps to build a useful model of input data, data collection, identifying the distribution with data, parameter estimation, suggested estimators, goodness of fit tests, selection input model without data, covariance and correlation, multivariate and time series input models.

9. Verification and Validation of Simulation Model: Introduction, model building, verification of simulation models, calibration and validation of models:- validation process, face validity, validation of model, validating input-output transformation, t-test, power of test, input output validation using historical data and Turing test.

10. Output Analysis: Types of simulations with respect to output analysis, stochastic nature of output data, measure of performance and their estimation, output analysis of terminating simulators, output analysis for steady state simulation.

11. Case Studies: Simulation of manufacturing systems, Simulation of Material Handling system, Simulation of computer systems, Simulation of super market, Cobweb model, and any service sectors.

Text Book: Banks J., Carson J. S., Nelson B. L., and Nicol D. M., “Discrete Event System Simulation”, 3rd edition, Pearson Education, 2001. Reference Books:

1. Gordon Geoffrey, “System Simulation”, 2nd edition, PHI, 1978.

2. Law A. M., and Kelton, W. D., “Simulation Modeling and Analysis”, 3rd edition, McGraw-Hill, 2000.

3. Narsing Deo, “System Simulation with Digital Computer”, PHI.

4. Frank L. Severance, “System Modeling and Simulation”

5. Trivedi K. S., “Probability and Statistics with Reliability, Queueing, and Computer Science Applications”, PHI, 1982.

6. Wadsworth G. P., and Bryan, J. G., “Introduction to Probability and Random Variables”, McGraw-Hill, 1960.

7. Donald W. Body, “System Analysis and Modeling”, Academic Press Harcourt India.

8. Bernard, “Theory Of Modeling and Simulation”

9. Levin & Ruben, “Statistics for Management”.

10. Aczel & Sounderpandian, “Business Statistics”.

Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks

The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment list The experiments should be implemented using Excel, simulation language like GPSS and/or any simulation packages. Case studies from the reference book can be used for experiment.

1. Single Server System

2. Multi serve system like Able – Baker

3. (M, N) – Inventory System

4. Dump Truck Problem

5. Job-Shop Model

6. Manufacturing System

7. Cafeteria

8. Telecommunication System

9. Uniformity Testing

10. Independence Testing

Mumbai University-Fourth / Final Year -Semester VII Information Technology Syllabus (Revised) DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE

DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	–
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		–	–
ORAL		–	25
TERM WORK		–	25
Prerequisite: Data Base Management System
Objective: Today is the era characterized by Information Overload – Minimum knowledge. Every business must rely extensively on data analysis to increase productivity and survive competition. This course provides a comprehensive introduction to data mining problems concepts with particular emphasis on business intelligence applications. The three main goals of the course are to enable students to: 1. Approach business problems data-analytically by identifying opportunities to derive business value from data. 2. know the basics of data mining techniques and how they can be applied to extract relevant business intelligence.

1. Introduction to Data Mining: Motivation for Data Mining, Data Mining-Definition & Functionalities, Classification of DM systems, DM task primitives, Integration of a Data Mining system with a Database or a Data Warehouse, Major issues in Data Mining.

2. Data Warehousing – (Overview Only): Overview of concepts like star schema, fact and dimension tables, OLAP operations, From OLAP to Data Mining.

3. Data Preprocessing: Why? Descriptive Data Summarization, Data Cleaning: Missing Values, Noisy Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Data Discretization and Concept hierarchy generation for numerical and categorical data.

4. Mining Frequent Patterns, Associations, and Correlations: Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules, Frequent Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods, The Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, Frequent Itemsets without Candidate Generation using FP Tree, Mining Multilevel Association Rules, Mining Multidimensional Association Rules, From Association Mining to Correlation Analysis, Constraint-Based Association Mining.

5. Classification & Prediction: What is it? Issues regarding Classification and prediction:

• Classification methods: Decision tree, Bayesian Classification, Rule based

• Prediction: Linear and non linear regression

Accuracy and Error measures, Evaluating the accuracy of a Classifier or Predictor.

6. Cluster Analysis: What is it? Types of Data in cluster analysis, Categories of clustering methods, Partitioning methods – K-Means, K-Mediods. Hierarchical Clustering- Agglomerative and Divisive Clustering, BIRCH and ROCK methods, DBSCAN, Outlier Analysis

7. Mining Stream and Sequence Data: What is stream data? Classification, Clustering Association Mining in stream data. Mining Sequence Patterns in Transactional Databases.

8. Spatial Data and Text Mining: Spatial Data Cube Construction and Spatial OLAP, Mining Spatial Association and Co-location Patterns, Spatial Clustering Methods, Spatial Classification and Spatial Trend Analysis. Text Mining Text Data Analysis and Information Retrieval, Dimensionality Reduction for Text, Text Mining Approaches.

9. Web Mining: Web mining introduction, Web Content Mining, Web Structure Mining, Web Usage mining, Automatic Classification of web Documents.

10. Data Mining for Business Intelligence Applications: Data mining for business Applications like Balanced Scorecard, Fraud Detection, Clickstream Mining, Market Segmentation, retail industry, telecommunications industry, banking & finance and CRM etc.

Text Books:

1. Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 2nd Edition

2. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education

Reference Books:

1. MacLennan Jamie, Tang ZhaoHui and Crivat Bogdan, “Data Mining with Microsoft SQL Server 2008”, Wiley India Edition.

2. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”, Wiley India.

3. Michael Berry and Gordon Linoff “Data Mining Techniques”, 2nd Edition Wiley Publications.

4. Alex Berson and Smith, “Data Mining and Data Warehousing and OLAP”, McGraw Hill Publication.

5. E. G. Mallach, “Decision Support and Data Warehouse Systems”, Tata McGraw Hill.

6. Michael Berry and Gordon Linoff “Mastering Data Mining- Art & science of CRM”, Wiley Student Edition

7. Arijay Chaudhry & P. S. Deshpande, “Multidimensional Data Analysis and Data Mining Dreamtech Press

8. Vikram Pudi & Radha Krishna, “Data Mining”, Oxford Higher Education.

9. Chakrabarti, S., “Mining the Web: Discovering knowledge from hypertext data”,

10. M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis (ed.), “Fundamentals of Data Warehouses”, Springer-Verlag, 1999.

Term Work: Term work shall consist of at least 10 experiments covering all topics Term work should consist of at least 6 programming assignments and one mini project in Business Intelligence and two assignments covering the topics of the syllabus. One written test is also to be conducted. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks

Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment List

1. Students can learn to use WEKA open source data mining tool and run data mining algorithms on datasets.

2. Program for Classification – Decision tree, Naïve Bayes using languages like JAVA

3. Program for Clustering – K-means, Agglomerative, Divisive using languages like JAVA

4. Program for Association Mining using languages like JAVA

5. Web mining

6. BI projects: any one of Balanced Scorecard, Fraud detection, Market Segmentation etc.

7. Using any commercial BI tool like SQLServer 2008, Oracle BI, SPSS, Clementine, and XLMiner etc.

ORAL EXAMINATION An oral examination is to be conducted based on the above syllabus.

Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised) DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE

DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	–
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		–	–
ORAL		–	25
TERM WORK		–	25
Prerequisite: Data Base Management System
Objective: Today is the era characterized by Information Overload – Minimum knowledge. Every business must rely extensively on data analysis to increase productivity and survive competition. This course provides a comprehensive introduction to data mining problems concepts with particular emphasis on business intelligence applications. The three main goals of the course are to enable students to: 1. Approach business problems data-analytically by identifying opportunities to derive business value from data. 2. know the basics of data mining techniques and how they can be applied to extract relevant business intelligence.

1. Introduction to Data Mining: Motivation for Data Mining, Data Mining-Definition & Functionalities, Classification of DM systems, DM task primitives, Integration of a Data Mining system with a Database or a Data Warehouse, Major issues in Data Mining.

2. Data Warehousing – (Overview Only): Overview of concepts like star schema, fact and dimension tables, OLAP operations, From OLAP to Data Mining.

3. Data Preprocessing: Why? Descriptive Data Summarization, Data Cleaning: Missing Values, Noisy Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Data Discretization and Concept hierarchy generation for numerical and categorical data.

4. Mining Frequent Patterns, Associations, and Correlations: Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules, Frequent Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods, The Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, Frequent Itemsets without Candidate Generation using FP Tree, Mining Multilevel Association Rules, Mining Multidimensional Association Rules, From Association Mining to Correlation Analysis, Constraint-Based Association Mining.

5. Classification & Prediction: What is it? Issues regarding Classification and prediction:

• Classification methods: Decision tree, Bayesian Classification, Rule based

• Prediction: Linear and non linear regression

Accuracy and Error measures, Evaluating the accuracy of a Classifier or Predictor.

6. Cluster Analysis: What is it? Types of Data in cluster analysis, Categories of clustering methods, Partitioning methods – K-Means, K-Mediods. Hierarchical Clustering- Agglomerative and Divisive Clustering, BIRCH and ROCK methods, DBSCAN, Outlier Analysis

7. Mining Stream and Sequence Data: What is stream data? Classification, Clustering Association Mining in stream data. Mining Sequence Patterns in Transactional Databases.

8. Spatial Data and Text Mining: Spatial Data Cube Construction and Spatial OLAP, Mining Spatial Association and Co-location Patterns, Spatial Clustering Methods, Spatial Classification and Spatial Trend Analysis. Text Mining Text Data Analysis and Information Retrieval, Dimensionality Reduction for Text, Text Mining Approaches.

9. Web Mining: Web mining introduction, Web Content Mining, Web Structure Mining, Web Usage mining, Automatic Classification of web Documents.

10. Data Mining for Business Intelligence Applications: Data mining for business Applications like Balanced Scorecard, Fraud Detection, Clickstream Mining, Market Segmentation, retail industry, telecommunications industry, banking & finance and CRM etc.

Text Books:

1. Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 2nd Edition

2. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education

Reference Books:

1. MacLennan Jamie, Tang ZhaoHui and Crivat Bogdan, “Data Mining with Microsoft SQL Server 2008”, Wiley India Edition.

2. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”, Wiley India.

3. Michael Berry and Gordon Linoff “Data Mining Techniques”, 2nd Edition Wiley Publications.

4. Alex Berson and Smith, “Data Mining and Data Warehousing and OLAP”, McGraw Hill Publication.

5. E. G. Mallach, “Decision Support and Data Warehouse Systems”, Tata McGraw Hill.

6. Michael Berry and Gordon Linoff “Mastering Data Mining- Art & science of CRM”, Wiley Student Edition

7. Arijay Chaudhry & P. S. Deshpande, “Multidimensional Data Analysis and Data Mining Dreamtech Press

8. Vikram Pudi & Radha Krishna, “Data Mining”, Oxford Higher Education.

9. Chakrabarti, S., “Mining the Web: Discovering knowledge from hypertext data”,

10. M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis (ed.), “Fundamentals of Data Warehouses”, Springer-Verlag, 1999.

Term Work: Term work shall consist of at least 10 experiments covering all topics Term work should consist of at least 6 programming assignments and one mini project in Business Intelligence and two assignments covering the topics of the syllabus. One written test is also to be conducted. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks

Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment List

1. Students can learn to use WEKA open source data mining tool and run data mining algorithms on datasets.

2. Program for Classification – Decision tree, Naïve Bayes using languages like JAVA

3. Program for Clustering – K-means, Agglomerative, Divisive using languages like JAVA

4. Program for Association Mining using languages like JAVA

5. Web mining

6. BI projects: any one of Balanced Scorecard, Fraud detection, Market Segmentation etc.

7. Using any commercial BI tool like SQLServer 2008, Oracle BI, SPSS, Clementine, and XLMiner etc.

ORAL EXAMINATION An oral examination is to be conducted based on the above syllabus.