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Syllabus B Tech Computer Science Eighth Semester Data Mining CS8003

- by Team_Engineers
Computer-Science-Engineering-8

Syllabus B Tech Computer Science Eighth Semester Data Mining CS8003

The concepts developed in this course will aid in quantification of several concepts in Computer Science Engineering that have been introduced at the Engineering courses. Technology is being increasingly based on the latest Syllabus B Tech Computer Science Eighth Semester Data Mining CS8003 is given here.

The objective of this course “Syllabus B Tech Computer Science Eighth Semester Data Mining CS8003“ is to develop ability and gain insight into the process of problem-solving, with emphasis on thermodynamics. Specially in following manner: Apply conservation principles (mass and energy) to evaluate the performance of simple engineering systems and cycles. Evaluate thermodynamic properties of simple homogeneous substances. Analyze processes and cycles using the second law of thermodynamics to determine maximum efficiency and performance. Discuss the physical relevance of the numerical values for the solutions to specific engineering problems and the physical relevance of the problems in general and Critically evaluate the validity of the numerical solutions for specific engineering problems. More precisely, the objectives are:

  • To enable young technocrats to acquire mathematical knowledge to understand Laplace transformation, Inverse Laplace transformation and Fourier Transform which are used in various branches of engineering.
  • To introduce effective mathematical tools for the Numerical Solutions algebraic and transcendental equations.
  • To acquaint the student with mathematical tools available in Statistics needed in various field of science and engineering.

CS 8003 – Data Mining

Unit 1
Introduction, to Data warehousing, needs for developing data Warehouse, Data warehouse systems and its Components, Design of Data Warehouse, Dimension and Measures, Data Marts:-Dependent Data Marts, Independents Data Marts & Distributed Data Marts, Conceptual Modeling of Data Warehouses:-Star Schema, Snowflake Schema, Fact Constellations. Multidimensional Data Model & Aggregates.
Unit 2
OLAP, Characteristics of OLAP System, Motivation for using OLAP, Multidimensional View and Data Cube, Data Cube Implementations, Data Cube Operations, Guidelines for OLAP Implementation, Difference between OLAP & OLTP, OLAP Servers:- ROLAP, MOLAP, HOLAP Queries.
Unit 3
Introduction to Data Mining, Knowledge Discovery, Data Mining Functionalities, Data Mining System categorization and its Issues. Data Processing :- Data Cleaning, Data Integration and Transformation. Data Reduction, Data Mining Statistics. Guidelines for Successful Data Mining.
Unit 4
Association Rule Mining:-Introduction, Basic, The Task and a Naïve Algorithm, Apriori Algorithms, Improving the efficiency of the Apriori Algorithm, Apriori-Tid, Direct Hasing and Pruning(DHP),Dynamic Itemset Counting (DIC), Mining Frequent Patterns without Candidate Generation(FP-Growth),Performance Evaluation of Algorithms.
Unit 5
Classification:-Introduction, Decision Tree, The Tree Induction Algorithm, Split Algorithms Based on Information Theory, Split Algorithm Based on the Gini Index, Overfitting and Pruning, Decision Trees Rules, Naïve Bayes Method. Cluster Analysis:- Introduction, Desired Features of Cluster Analysis, Types of Cluster Analysis Methods:- Partitional Methods, Hierarchical Methods, Density- Based Methods, Dealing with Large Databases. Quality and Validity of Cluster Analysis Methods.

Books Recommended

1. Berson: Data Warehousing & Data Mining &OLAP , TMH
2. Jiawei Han and Micheline Kamber, Data Mining Concepts & Techniques, Elsevier Pub.
3. Arun.K.Pujari, Data Mining Techniques, University Press.
4. N.P Gopalan: Data Mining Technique & Trend, PHI
5. Hand, Mannila & Smith: Principle of Data Mining, PHI
6. Tan, Introduction to Data Mining, Pearson Pub.

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