Homework 3 discussion, ECE662, Spring 2010, Prof. Boutin
I found a MATLAB function for finding the k-nearest neighbors (kNN) within a set of points, which could be useful for homework 3.
I tried it and it works well. I did some experiments using the Wine data set of UCI (http://archive.ics.uci.edu/ml/datasets.html). I used attributes 1 and 7 of the red wine data set (red points) and the white wine data set (grey points). For this simple experiment, I used only the first 100 data points of each set. The following figures show the classification regions using k=1, 3, 7. The red wine region is brown and the white wine region is white. The regions are constructed using MATLAB's contourf function.
--ilaguna 15:40, 7 April 2010 (UTC)
"Traveling Salesman Problem" (TSP) is one of the interesting applications of K-nearest neighbors (KNN) method. As you know, TSP is one of the most important problem in algorithm design area. KNN is a sub-optimal approach for this problem. You can find more info about it in this page http://en.wikipedia.org/wiki/Nearest_neighbour_algorithm and there is an interesting java applet in this webpage: http://www.wiley.com/college/mat/gilbert139343/java/java09_s.html.
Have fun, Golsa
