This article demonstrates an illustration of K-means clustering on a sample random data using open-cv library.
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.
- Exploratory Data Analysis in Python | Set 1
- Data analysis and Visualization with Python
- Exploratory Data Analysis in Python
- Python | Data analysis using Pandas
- Multidimensional data analysis in Python
- Data Analysis and Visualization with Python | Set 2
- Exploratory Data Analysis in Python | Set 2
- Python | Math operations for Data analysis
- Replacing strings with numbers in Python for Data Analysis
- Elbow Method for optimal value of k in KMeans
- ML | Hierarchical clustering (Agglomerative and Divisive clustering)
- DBSCAN Clustering in ML | Density based clustering
- Difference between CURE Clustering and DBSCAN Clustering
- Python - Test Similar Data Type in Tuple
- Python - Random Sample Training and Test Data from dictionary
- Data Analysis with SciPy
- Violin Plot for Data Analysis
- Data Analysis in Financial Market – Where to Begin?
- Python | Clustering, Connectivity and other Graph properties using Networkx
- Text Analysis in Python 3
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to firstname.lastname@example.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.