kmeans.rst

kmeans

Table of contents

• Description
• Syntax
• Example: Clustering of Iris Dataset

Description

The kmeans command applies the kmeans algorithm in the ml-commons plugin on the search result returned by a PPL command.

Syntax

kmeans <centroids> <iterations> <distance_type>

• centroids: optional. The number of clusters you want to group your data points into. The default value is 2.
• iterations: optional. Number of iterations. The default value is 10.
• distance_type: optional. The distance type can be COSINE, L1, or EUCLIDEAN, The default type is EUCLIDEAN.

Example: Clustering of Iris Dataset

The example shows how to classify three Iris species (Iris setosa, Iris virginica and Iris versicolor) based on the combination of four features measured from each sample: the length and the width of the sepals and petals.

PPL query:

os> source=iris_data | fields sepal_length_in_cm, sepal_width_in_cm, petal_length_in_cm, petal_width_in_cm | kmeans centroids=3
+--------------------+-------------------+--------------------+-------------------+-----------+
| sepal_length_in_cm | sepal_width_in_cm | petal_length_in_cm | petal_width_in_cm | ClusterID |
|--------------------+-------------------+--------------------+-------------------+-----------|
| 5.1                | 3.5               | 1.4                | 0.2               | 1         |
| 5.6                | 3.0               | 4.1                | 1.3               | 0         |
| 6.7                | 2.5               | 5.8                | 1.8               | 2         |
+--------------------+-------------------+--------------------+-------------------+-----------+