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Presentation of data mining

  a. presentation 01 (unit 1)

                    b. presentation 02

                    c. presentation 03

Syllabus of 8th semester

1: computer graphics

 2: Data warehouse and data minig

Course Code: MCET 302                                                       Title: Data Warehousing & Data Mining [SGGSWU- DCSE]                                                                                                             L/T/P: 3/1/1
UNIT I                                                                                  (13hrs) Introduction: Introduction to Data Mining, Kind of Data to be mined, Data Mining Functionalities, Pattern Interestingness, Classification of Data Mining System, Major Issues in Data Mining
Data Warehouse and OLAP: Data warehouse, Difference from traditional databases, Multidimensional data model, Schema for Multi dimensional model, measures, concept hierarchies, OLAP operations, starnet query model, Data Warehouse architecture, ROLAP, MOLAP, HOLAP, Data Warehouse Implementation, Data Cube, Metadata Repositories, OLAM                                                           UNIT II                                                                                   (13hrs)
Data Objects and Attribute Types:  Nominal, Binary, Ordinal, Numeric, Discrete and Continuous Attributes Basic Statistical Descriptions of Data:  Measuring the  Central Tendency and  Dispersion of data Data Processing: Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and concept hierarchy
UNIT III                                                                                 (13hrs)
Association Rules: Market basket analysis, Frequent, Closed Itemsets, Frequent Pattern Mining, The Apriori  Algorithm, Improving the efficiency of Apriori, Mining Frequent Itemsets without Candidate Generation, Mining various kinds of association rules,  Correlational analysis
Classification and Clustering : Classification and prediction, Decision tree induction, Bayesian classification, k-nearest neighbour classification, Cluster analysis, Types of data in clustering, categorization of clustering methods
UNIT IV                                                                              (13hrs) Introduction of Mining Complex Data: Complex data objects, Mining spatial databases, Multimedia databases, Time Series and Sequence Databases, Text databases and World Wide Web, Data Mining Applications and Trends.
Reference  Books: J.Han and M. Kamber: Data Mining: Concepts and Techniques By Morgan Kaufman publishers, Harcourt India pvt. Ltd. Latest Edition Dunham: Data Mining Introductory and Advance Topics, Pearson Education, Latest Edition Berson : Data Mining By TMH

 

3: Informtion Retrieval

 4: wireless communiction and networks

unit 3 of CD and DOS

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SPECIAL THANKS TO SHRI KRISHNA CYBER CAFE
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Compiler Design notes

Semester 7th Updated

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