the Optimal Number of Clusters

Functionalities

Determine the best 'k' for k-means clustering algorithm by the gap statistic

  • Use the sample data or upload your data from CSV files.
  • Draw the plot of the gap statistic.The intersection of the blue dotted line and horizontal axis (X-axis) indicates the best 'k'.
  • Display the input data and calculate the gap statistic.

Case Example

We selected the first four columns of the iris data as an example of cluster analysis.

Please follow the Steps, and Outputs will give real-time analytical results.


Step 1. Data Preparation

2. Choose whether you need standardized your data

3. Choose the features which are used in clusting

Step 2. Result visualisation

1. Set the max clusting number in the output image

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Output

Plot:

Input Confirm:

Results:


            

Download:

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Kmeans

Functionalities

Partition your data into k classes with k-means clustering.

  • Use the sample data or upload your data from CSV files.
  • Choose a visualization method to draw the plot of the K-means clustering. Dots of the same color indicate that they belong to the same class.
  • Display the input data and visualize the results.

Case Example

We selected the first four columns of the iris data as an example of cluster analysis.

Please follow the Steps, and Outputs will give real-time analytical results.


Step 1. Data Preparation

2. Choose whether you need standardized your data

3. Choose the features which are used in clusting

Step 2. Model Fitting

1. Determine the number of clusters

2. Choose the Visualization method

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Output

Input Confirm:

Results:


            

Download:

Download

Hierarchical Clustering

Functionalities

Partition your data with hierarchical clustering.

  • Use the sample data or upload your data from CSV files.
  • Choose a visualization method to draw the plot of the K-means clustering.The sample in the dotted boxes belong to the same cluster.
  • Display the input data and visualize the results.

Case Example

We selected the first four columns of the iris data as an example of cluster analysis.

Please follow the Steps, and Outputs will give real-time analytical results.


Step 1. Data Preparation

2. Choose whether you need standardized your data

3. Choose the features which are used in clusting

Step 2. Model Fitting

1. Determine the number of clusters

2. Determine the distance measure to be used

3. Determine the agglomeration method to be used

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Output

Plot:

Input Confirm:

Results:


            

Download:

Download