Analysis of test data using K-Means Clustering in Python
This article demonstrates an illustration of K-means clustering on a sample random data using open-cv library.
Pre-requisites: Numpy, OpenCV, matplot-lib
Let’s first visualize test data with Multiple Features using matplot-lib tool.
Here ‘Z’ is an array of size 100, and values ranging from 0 to 255. Now, reshaped ‘z’ to a column vector. It will be more useful when more than one features are present. Then change the data to np.float32 type.
Now, apply the k-Means clustering algorithm to the same example as in the above test data and see its behavior.
1) First we need to set a test data.
2) Define criteria and apply kmeans().
3) Now separate the data.
4) Finally Plot the data.
This example is meant to illustrate where k-means will produce intuitively possible clusters.
1) Identifying Cancerous Data.
2) Prediction of Students’ Academic Performance.
3) Drug Activity Prediction.
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