文件名称:kMeansCluster
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kMeansCluster - Simple k means clustering algorithm
Author: Kardi Teknomo, Ph.D.
Purpose: classify the objects in data matrix based on the attributes
Criteria: minimize Euclidean distance between centroids and object points
For more explanation of the algorithm, see http://people.revoledu.com/kardi/tutorial/kMean/index.html
Output: matrix data plus an additional column represent the group of each object
-kMeansCluster - Simple k means clustering algorithm
Author: Kardi Teknomo, Ph.D.
Purpose: classify the objects in data matrix based on the attributes
Criteria: minimize Euclidean distance between centroids and object points
For more explanation of the algorithm, see http://people.revoledu.com/kardi/tutorial/kMean/index.html
Output: matrix data plus an additional column represent the group of each object
Author: Kardi Teknomo, Ph.D.
Purpose: classify the objects in data matrix based on the attributes
Criteria: minimize Euclidean distance between centroids and object points
For more explanation of the algorithm, see http://people.revoledu.com/kardi/tutorial/kMean/index.html
Output: matrix data plus an additional column represent the group of each object
-kMeansCluster - Simple k means clustering algorithm
Author: Kardi Teknomo, Ph.D.
Purpose: classify the objects in data matrix based on the attributes
Criteria: minimize Euclidean distance between centroids and object points
For more explanation of the algorithm, see http://people.revoledu.com/kardi/tutorial/kMean/index.html
Output: matrix data plus an additional column represent the group of each object
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下载文件列表
kMeansCluster.m