Simple and crude understanding and implementation of machine learning clustering algorithm (III): clustering algorithm implementation process, k-means clustering step, case

Clustering Algorithm

learning target

• Master clustering algorithm implementation process
• We know K-means algorithm theory
• We know evaluation model clustering algorithm
• The advantages and disadvantages of K-means
• Understand way clustering algorithm optimization
• Application Kmeans achieve clustering task
  

6.3 clustering algorithm implementation process

k-means actually contains two elements:

K: The number of initial central point (plan number of clusters)

means: to find the center point of the other data points from the average value of

1 k-means clustering step

• 1, a random set point in feature space as the K initial cluster centers
• 2, the distance K is calculated for each of the other central point, unknown point select the nearest cluster center as a marker category
• 3, then after the cluster center against the tag, a recalculation of the center point of each cluster (average)
• 4, if the calculation results with the original new center point as the center point (centroid no longer moving), then ends, otherwise re-Step Process

FIG explained achieved by the following process:

FIG dynamic effects clustering k

2 Case Exercises

• Case:

• 1, a random set point in feature space as the K initial cluster centers (in this case provided p1 and p2)

2, the distance K is calculated for each of the other central point, unknown point select the nearest cluster center as a marker category

3, then after the cluster center against the tag, a recalculation of the center point of each cluster (average)

4, if the calculated new center point as the original center point (centroid no longer moving), then ends, otherwise re-Step Process [after judgment, need to repeat the steps above to start a new round of iteration]

5, when the same result each iteration that convergence, clustering is completed, K-Means will be stopped, can not fall into the election process has been the center of mass.

3 Summary

Process :

• Pre- determined constant K , K means constant final number of clusters category;
• First randomly selected as the initial point of the centroid , and by calculating a similarity between each sample and the centroid (here, the Euclidean distance), the normalized sample points to the most similar class,
• Next, recalculated each cluster centroid (i.e. cluster center), repeat this process until the centroids no longer change ,
• Ultimately determine the category of each sample belongs and the centroid of each class.

Note :

• Because every time the calculation of similarity between the sample and the centroid of each, so that on large data sets, K-Means algorithm convergence speed slower.