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Neural Networks Project

Prezi Presentation that describes a proposed algorithm that enhances the kmeans

Marwan Hefny

on 16 January 2013

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Transcript of Neural Networks Project

Optimization of Code Book in Vector Quantization Proposed Algorithm Pattern Recognition Introduction Features Vector Quantization Clustering Hierarchical Non-Hierarchical Agglomerative Clustering Methods Divisive Partitioning Clumping K-Means Clustering Initialize the Patterns
(Optional) Stops the program Initially One Cluster Values that will save the Cluster with the Maximum SD Looping through the features Looping through the Patterns Calculating the mean and SD of specific feature of the current cluster Getting to know the feature of the maximum SD Getting the mean of the big cluster Getting the values of the cluster that satisfied the condition Getting the elements' position and number. Splitting the Cluster Getting the Centers of each cluster Getting the Mue Proposed Algorithm Step 2 Step 1 Step 3 Step 5 Step 4 Step 6 Step 7 Initialize a set of cluster S = {C1} and set K = 1 where CK = {x1, x2, . . ., xm}. Compute the mean (M) of standard deviations of cluster CK. Find Cj for some j from S which is having a maximum standard deviation say σji. If σji > M then split Cj into Cj and CK+1. If there exist no σji > M then go to step 6, otherwise go to Step 3. Compute cluster center z1, z2, . . ., zK for each cluster Ci. Stop Code Testing
Conclusion Stopping Flag
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