NEC B.Tech CSE 3-1 DWDM Video Tutorial for Important Topics

Here some NEC CSE 3-1 DWDM Video Tutorial for Important Topics. By reading/watching these topics you can good marks in your external exams. Follow or book marks this blog for your exam preparation. 

Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information. Companies use data mining software to learn more about their customers. It can help them to develop more effective marketing strategies, increase sales, and decrease costs.

Note: Click on the required topic to watch video. 

 

UNIT-I (Introduction to data mining)

1.Data Mining and Challenges

2. Data Mining and knowledge extraction

3.  Origin of Data Mining 

4. Attributes and Measurement, Types of attributes

5. Types of Data Sets --- Link-I  OR Link-II 

UNIT-II (Data:- Data Preprocessing)

1. Introduction to Data Preprocessing - Part-I 

2. Introduction to Data Preprocessing - Part-II 

3. Aggregation as Data Preprocessing

4. Sampling as Data Preprocessing

5. Dimensionality Reduction - Attribute Selection part-I

6. Dimensionality Reduction - Attribute Selection part-II

7. Feature Subset Selection  

8. Discretization and

9. Binarization

10. Measures of similarity and dissimilarity 

UNIT-III (Data Warehouse and OLAP Technology for Data Mining) 

1. What is a Data Warehouse - Concepts

2.  Data Warehouse characteristics

3. A Multidimensional Data Model: From tables to data cubes, Stars, snowflake, and fact constellations(schemas for multidimensional databases)

4. Examples for defining star, snowflake, and fact constellation schemas

5.Introducing concept hierarchies  

6. OLAP operations in the multidimensional data model 

7.  A starnet query model for querying multidimensional databases

8. Data Warehouse Architecture: Steps for the design and construction of data warehouses  

9. OLAP server architectures:-ROLAP, MOLAP, HOLAP

UNIT-IV (Classification)

1. General Approach to Solving a Classification Problem

2. How a Decision Tree Works

3. How to Build a Decision Tree 

4. Methods for Expressing Attribute Test Conditions

5. Measures for Selecting the Best Split  

6. Algorithm for Decision Tree Induction

7. Model Over fitting  

8. Evaluating the Performance of a Classifier:

   8.1.Cross Validation  

   8.2. Hold-Out Method 

   8.3. Random Sub sampling

   8.4. Bootstrap 

9. Bayesian classifier

10. Using Bayes theorem for classification

11. Naïve Bayesian classifier  

UNIT-IV (Association Analysis)

1. Apriori Algorithm

2. FP-Growth algorithms

3. Different Types of Clustering

4. K-Means Clustering  

   4.1. Theory 

   4.2. Problem 

5. Agglomerative Hierarchical Clustering

6. DBSCAN Clustering