Detail Interpretation of K-means Centroid Table

wwahidi2018

Hi everyone,

I know the concept of clustering and how it works. However, I am a little confused with the following case.

 

I have a dataset with only two features, product and price. The Product is categorical with limited possible values. The Price  is continous and represents products prices.

 

Basic statistical analysis classify Product 1 and Product 2 into a cluster of high prices; and Product 3 and Product 4 into another cluster of low prices. Out of curiousity I just wanted to understand this case using clustering techniques in Rapidminer. Therefore, I created the following process.

<?xml version="1.0" encoding="UTF-8"?><process version="8.2.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="8.2.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="8.2.000" expanded="true" height="68" name="Retrieve products" width="90" x="45" y="85">
        <parameter key="repository_entry" value="../data/products"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="7.1.001" expanded="true" height="103" name="Nominal to Numerical" width="90" x="246" y="85">
        <list key="comparison_groups"/>
      </operator>
      <operator activated="true" class="concurrency:k_means" compatibility="8.2.000" expanded="true" height="82" name="Clustering" width="90" x="447" y="85">
        <parameter key="max_runs" value="50"/>
      </operator>
      <operator activated="true" class="extract_prototypes" compatibility="8.2.000" expanded="true" height="82" name="Extract Cluster Prototypes" width="90" x="581" y="85"/>
      <connect from_op="Retrieve products" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Clustering" to_port="example set"/>
      <connect from_op="Clustering" from_port="cluster model" to_op="Extract Cluster Prototypes" to_port="model"/>
      <connect from_op="Extract Cluster Prototypes" from_port="example set" to_port="result 1"/>
      <connect from_op="Extract Cluster Prototypes" from_port="model" to_port="result 2"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>

  The above process produces the Centroid Table below.

The followings are my questions, please bear with me if my questions are basic:

1. What are the values of cluster_0 and cluster_1 columns and how to interpret them?
2. Can we compare and interpret if cluster_0's value > cluster_1's value for a particular product, then that product belongs to cluster_0, otherwise it belongs to cluster_1?
3. My final question is how to intrepret the following plot that is produced by the process? and does it make sense to use clusering on such datasets with one or two categorical features and one continues feature?

Thanks a million in advance,

MartinLiebig

Hi @wwahidi2018,

 

KMeans searches for the k means (or centers of gravity, if you want) in a data set. The table gives you the coordinates of these.

 

I think in your case you want to:

• Normalize your data first
• Use k-Medoids and not k-means

K-Medoids ensures that the center is always an existing item. So you will get the most prototypical item as a center.

 

Cheers,

Martin