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Markov Chains || Step-By-Step || ~xRay Pixy

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Learn Markov Chains step-by-step using real-life examples. Video Chapters: Markov Chains 00:00 Introduction 00:19 Topics Covered 01:49 Markov Chains Applications 02:04 Markov Property 03:18 Example 1 03:54 States, State Space, Transition Probabilities 06:17 Transition Matrix 08:17 Example 02 09:17 Example 03 10:26 Example 04 12:25 Example 05 14:16 Example 06 16:49 Example 07 18:11 Example 08 24:56 Conclusion
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Whale Optimization Algorithm for Association Rule Mining.

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 Whale Optimization Algorithm for association rule mining.

Input: Number of Maximum Iteration and Population Size, Minsupport, minconfedence. 

Step 01: Initialize the population size for n search agents.[Xi(i=1,2,3,...n)]

Step 02: Initialize i, A, C, L, and p.

Step 03: Compute the fitness value of each search agent/whale.

Step 04: X* = the best rule

Step 05: While (CurrentIteration <= MaximumIteration )

Step 06: Update a, A, C, L and p.

Step 07: For all whale poplation check

         if (p<0.5)

if(|A|<1)

   For each Item in the solution Xi.

   Update Items.

Else if(|A|=1)

    Select a random Item in Xi.

Update Items.

End if

       For each item in the solution Xi.

If the Item is odd, it belongs to the antecedent,

Otherwise, it belongs to the consequence.

End for

Step 08: Calculate the fitness of each search agent.

Step 09: Update X* if there is a better solution.

Step 11: Iteration = currentIteration + 1

Step 12:    End While

Step 13:    Return X*

1.)  WHALE OPTIMIZATION ALGORITHM - NUMERICAL EXAMPLE

LINK - https://youtu.be/kq1K3mdDZt0

                 2.)    WHALE OPTIMIZATION ALGORITHM - MATHEMATICAL MODEL

Click Here - > Watch Now

https://youtu.be/f7hvvDkLoHs

#Metaheuristic #Algorithms
Meta-heuristic Algorithms

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