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Data Warehouse and Data Mining [Module 1]:- Data Warehousing Fundamentals 10
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                Introduction to Data Warehouse 11 minLecture1.1
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                Meta Data 05 minLecture1.2
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                Data Mart 06 minLecture1.3
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                Data Warehouse Architecture 07 minLecture1.4
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                How to Draw Star , Smowflake and Fack Constelation Basics 10 minLecture1.5
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                Numericals on Star , Snowflake and Fact Constelation [ Part 1 ] 16 minLecture1.6
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                Numericals on Star , Snowflake and Fact Constelation [ Part 2 ] 11 minLecture1.7
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                What is Olap Operations 08 minLecture1.8
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                OLAP VS OLTP 08 minLecture1.9
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                Extract Transform and Load (ETL) 09 minLecture1.10
 
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Data Warehouse and Data Mining [Module 2]:- Introduction to Data Mining, Data Exploration 7
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                Introduction to Data Mining and Architecture 10 minLecture2.1
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                KDD Process in Data Mining 09 minLecture2.2
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                Types of Attribute 09 minLecture2.3
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                Data Visualization Part #1 11 minLecture2.4
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                Data Visualization Part #2 11 minLecture2.5
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                Data Preprocessing Part #1 17 minLecture2.6
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                Data Preprocessing Part #2 09 minLecture2.7
 
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Data Warehouse and Data Mining [Module 3]:- Classification 4
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                Naive Bayes Numerical Solved Example [ Nov 2022 ] [ 10 Marks ]Lecture3.1
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                Naive Bayes Part #2 25 minLecture3.2
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                Naive Bayes Part #1 18 minLecture3.3
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                Decision Tree 24 minLecture3.4
 
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Data Warehouse and Data Mining [Module 4]:- Clustering 6
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                K Mean clustering with Example [ Type 1 ] 12 minLecture4.1
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                K Mean clustering with Example [ Type 2 ] 23 minLecture4.2
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                K Mean Numerical Solved Example [ May 2022 , May 2023 ] [ 10 Marks ]Lecture4.3
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                K Medoid with Example 21 minLecture4.4
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                Agglomerative Clustering Algorithm with Example 13 minLecture4.5
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                Agglomerative Adjacency Matrix using Euclidean Distance with Solved Example 05 minLecture4.6
 
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Data Warehouse and Data Mining [Module 5]:-Mining frequent patterns and associations 2
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                Apiori Algoirthm with Solved Example 12 minLecture5.1
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                FP Tree Algorithm with Solved Example 15 minLecture5.2
 
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Data Warehouse and Data Mining [Module 6]:- Web Mining 4
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                Web content mining 06 minLecture6.1
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                Web Mining 07 minLecture6.2
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                Page rank Algorithm 06 minLecture6.3
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                HITTS Algorithm [ Hubs and Authority ] 12 minLecture6.4
 
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Data Warehouse and Data Mining [Notes]:- 2
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                Data Warehouse and Data Mining NOTESLecture7.1
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                Data Warehouse and Data Mining ImportanceLecture7.2
 
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Data Warehouse and Data Mining [Viva Question]:- 6
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                IntroductionLecture8.1
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                Introduction to Data Mining,Data Exploration and Data Pre-processingLecture8.2
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                ClusteringLecture8.3
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                ClassificationLecture8.4
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                Mining frequent patterns and associationsLecture8.5
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                Web MiningLecture8.6
 
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Data Warehouse and Data Mining [ Importance ] :- 6
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                Data Warehouse and Data Mining ImportanceLecture9.1
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                Data Warehouse and Data Mining NOTESLecture9.2
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                DWMLecture9.3
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                K Mean Numerical Solved ExampleLecture9.4
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                Naive Bayes Numerical Solved ExampleLecture9.5
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                Star and Snowflake Schema NumericalsLecture9.6
 
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