17CS651: Data Mining and Data Warehousing
41 videos • 1,816 views • by Manoj Taleka
1
Introduction to Rule-Based Classifiers
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2
How a Rule-Based Classifier Works?
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3
Rule Ordering Schemes
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4
Sequential Covering Algorithm
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5
Rule Growing Strategy
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6
Rule Evaluation for rule growing process
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7
RIPPER[Repeated Incremental Pruning to Produce Error Reduction]
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8
Indirect Methods for Rule Extraction
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9
Introduction to Nearest Neighbor Classifier
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10
k Nearest Neighbor Algorithm
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11
Introduction to Association Analysis
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12
Basic Terminology related to Association Analysis
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13
Relevance of Support and Confidence in Association Analysis
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14
Problem Formulation for Association Analysis
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15
Apriori Principle for Frequent Itemset Generation
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16
Candidate Generation Procedures
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17
Support Counting in Apriori Algorithm
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18
Factors Affecting Computational Complexity of Apriori Algorithm
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19
Rule Generation in Apriori Algorithm
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20
Alternative Methods for Frequent Itemset Generation
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21
FP Growth Algorithm for Association Analysis
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22
Frequent Itemset Generation in FP-Growth Algorithm
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23
Introduction to Evaluation of Association Patterns
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24
Objective Measures of Interestingness in Association Analysis
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25
Simpsons Paradox in Association Analysis
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26
Introduction to Cluster Analysis
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27
Different Types of Clustering
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28
Types of Clusters
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29
K-Means Algorithm
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30
Analysis of Steps in K-Means Algorithm
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31
Choosing Initial Centroids in K-Means Algorithm
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32
Additional Issues in K-Means:Part I
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33
Additional Issues in K-Means:Part II
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34
Agglomerative Hierarchical Clustering
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35
Specific Techniques in Agglomerative Hierarchical Clustering
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36
Issues in Hierarchical Clustering
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37
Introduction to Density Based Clustering
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38
Introduction to Density Based Clustering
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39
Introduction to Density Based Clustering
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40
Introduction to Density Based Clustering
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41
Introduction to Density Based Clustering
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