By Kardi Teknomo, PhD.
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This tutorial is an introduction to an instance based learning called KNearest 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. If you purchase the ebook of this tutorial, you can 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 are:
What is KNearest Neighbor (KNN) Algorithm?
How KNearest Neighbor (KNN) Algorithm works?
Numerical Example (hand computation)
KNN for Smoothing and Prediction
KNN for Interpolation, Smoothing
KNN for Extrapolation, Prediction, Forecasting
How do we use the spreadsheet for KNN?
Strength and Weakness of KNearest Neighbor Algorithm
Resources for K Nearest Neighbors Algorithm
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See Also:
K means clustering, Similarity Measurement, Reinforcement Learning (QLearning), Discriminant Analysis, Kernel Regression, Clustering, Decision Tree
This tutorial is copyrighted.
Preferable reference for this tutorial is
Teknomo, Kardi. KNearest Neighbors Tutorial. http:\\people.revoledu.com\kardi\
tutorial\KNN\
