Today, I want to show you a new interactive demo of the KNN (K-Nearest Neighbors) algorithm that I have added to my website. It is designed to be used for teaching purpose to illustrate how the K-Nearest Neighbors algorithm works.
You can try the KNN Interactive demo here. The interface is like this:
In the section 1 of the webpage, you can enter some data that is a list of records or instances to be used by KNN to make predictions. The first line is the list of attributes. Then, each following line is a record, wich is a list of values separated by single spaces. The values can be categorical or numerical.
Then, the value of K can be selected in section 2 of the webpage. For the purpose of teaching, values of K are restricted to be between 1 to 100.
Then, you can provide an instance to classify in section 3 of the webpage. The instance to classify is a list of attribute values but one of them must be replaced by ? meaning that we want to predict this attribute value using KNN.
Finally, by clicking Run KNN button, the result are displayed like this:
It indicates the K most similar instances, the calculated distances between those instances and the instance, and the predicted attribute value.
It is possible to run the demo with different values of K and different data to observe the result, which can be good for learning.
This tool is for teaching purpose. If you want to try a more efficient implementation in Java, you could try the one from the SPMF data mining software, which is free and open source.