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Clustering Algorithm is used to classify the data structure that not labeled at the beginning.
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K-means is one example of Clustering, and one of the most powerful and famous example of Clustering Algorithm to date.
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Two step in K-means
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Cluster Assignment Step
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Move Centroid Step
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First step(Cluster Assignment Step), build n number of cluster centroids(number of cluster that we want)
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Color which one is closer between cluster centroids
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Second step(Move Centroids Step), take average/mean of the distance of all the examples that has the same color as the centroid has, and move it to the result point
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Go back to Cluster Assignment Step, and recolor it once again
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Again, continue back to Move Centroid Step,
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Do it again to Cluster Assignment Step
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Iterate it over and over again until the cluster centroids don't move any further
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These are the mathematical step of K-means algorithm
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First let manually set K
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Remember that y value is not needed anymore
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without x0 convention
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First initialize all the cluster randomly
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Then create some vector represent examples that the ith element is assigned with the index number of cluster that closest to it.(find the shortest length)
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Then for every cluster, get all the vector assigned earlier, take average distance(mean) produced midpoint among same index, and move the cluster relative to the index
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upper K denotes max number of K
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lower k is index of cluster centroids
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x is m-dimensional vector
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if sometimes there's one cluster centroid that has no point, best to eliminate it. Then it resulting to K-1 init, or reiinitialized it, although the first one is more wisely
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how about non-separated clusters?
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Suppose we collected data from people that has correlated data between their weight and height
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How big the radius of clusters for each S,M,L?
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K-means still separate the data
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Now this where the Market Segmentation can be benefit from. From three separate group of people, we can design S to match S-group of people, and also for the case M for M-group and L for L-group. This way the company can build product, and segment its product for each specific groups that they decide.
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So we can implemented the K-means algorithm by now, but still discussed more deeply in the next video
