K-mean clustering
2016-08-23
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K-means is a clustering algorithm. The goal is to partition n data points into different K groups. Each n data points will be assigned to the nearest mean cluster. Each cluster is referred to as its & quot; prime & quot; or & quot; Center & quot;. In general, the original n data points of the cluster using k-means return rate k alone. A group of data points in a cluster are considered as & quot; more similar & quot; data points that belong to other clusters than each other. In our example, we will cluster the pixel strengths of RGB images. Given the mxn size of the image, we have mxn pixels, each of which consists of three parts: red, green and blue, respectively. We will treat these mxn pixels as our data points and use k-means clustering. Pixels belonging to a given cluster will be more similar in color than pixels belonging to a separate cluster.
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