K Nearest Neighbors Tutorial

By Kardi Teknomo, PhD.

This tutorial is an introduction to an instance based learning called K-Nearest Neighbor or KNN algorithm. KNN is part of supervised learning that has been used in many applications in the field of data mining, statistical pattern recognition, image processing and many others. Some successful applications are including recognition of handwriting, satellite image and EKG pattern. Instead of using sophisticated software or any programming language, I will use only spreadsheet functions of Microsoft Excel, without any macro. You can free download the spreadsheet companion of this tutorial.

First, you will learn KNN for classification, then we will extend the same method for smoothing and prediction in solving time series data.

Topics of this tutorial (click any of them to enter):

What is K-Nearest Neighbor (KNN) Algorithm?

How K-Nearest Neighbor (KNN) Algorithm works?

Numerical Example (hand computation)

KNN for Smoothing and Prediction

How do we use the spreadsheet for KNN?

Strength and Weakness of K-Nearest Neighbor Algorithm

Resources for K Nearest Neighbors Algorithm

Give your comments, questions or suggestions

See Also: K means clustering, Similarity Measurement, Reinforcement Learning (Q-Learning), Discriminant Analysis, Kernel Regression

This tutorial is copyrighted.

Preferable reference for this tutorial is

Teknomo, Kardi. K-Nearest Neighbors Tutorial. http:\\people.revoledu.com\kardi\ tutorial\KNN\