33:40
6LoWPAN Protocol
13:07
Need for optimization in IP for IoT
11:00
Adoption and Adaptation of IP
10:20
Business Case For IP
02:13
Introduction to Module 3
05:34
Introduction to Density Based Clustering
05:09
Issues in Hierarchical Clustering
12:29
Specific Techniques in Agglomerative Hierarchical Clustering
12:39
Agglomerative Hierarchical Clustering
14:58
Additional Issues in K-Means:Part II
11:34
Additional Issues in K-Means:Part I
15:22
Choosing Initial Centroids in K-Means Algorithm
13:36
Analysis of Steps in K-Means Algorithm
13:55
K-Means Algorithm
06:52
Types of Clusters
09:57
Different Types of Clustering
08:45
Introduction to Cluster Analysis
09:37
Simpsons Paradox in Association Analysis
35:25
Objective Measures of Interestingness in Association Analysis
09:23
Introduction to Evaluation of Association Patterns
19:52
Frequent Itemset Generation in FP-Growth Algorithm
11:24
FP Growth Algorithm for Association Analysis
15:29
Alternative Methods for Frequent Itemset Generation
19:57
Rule Generation in Apriori Algorithm
11:07
Factors Affecting Computational Complexity of Apriori Algorithm
06:42
Support Counting in Apriori Algorithm
15:27
Candidate Generation Procedures
10:38
Apriori Algorithm for Frequent Itemset Generation
10:42
Apriori Principle for Frequent Itemset Generation
06:40
Problem Formulation for Association Analysis
04:31
Relevance of Support and Confidence in Association Analysis
10:18
Basic Terminology related to Association Analysis
05:40
Introduction to Association Analysis
08:32
k Nearest Neighbor Algorithm
04:33
Introduction to Nearest Neighbor Classifier
07:34
Indirect Methods for Rule Extraction
08:18
RIPPER[Repeated Incremental Pruning to Produce Error Reduction]
13:09
Rule Evaluation for rule growing process
04:17
Rule Growing Strategy
09:01
Sequential Covering Algorithm
03:38
Rule Ordering Schemes
07:43
How a Rule-Based Classifier Works?
10:43
Introduction to Rule-Based Classifiers
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