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MA4102 – Data Mining and Neural Networks Nathan Ifill University of Leicester Image source: Antti Ajanki, “Example of k-nearest neighbor.

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Presentation on theme: "MA4102 – Data Mining and Neural Networks Nathan Ifill University of Leicester Image source: Antti Ajanki, “Example of k-nearest neighbor."— Presentation transcript:

1 MA4102 – Data Mining and Neural Networks Nathan Ifill ngi1@le.ac.uk University of Leicester Image source: Antti Ajanki, “Example of k-nearest neighbor classification”, 28 May 2007

2 k-nearest neighbour algorithm

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4  Classes with more frequent examples dominate predictions of unknown instances.  Assigning weights helps to remove this problem.  The algorithm can be computationally intensive depending on the size of the training set.

5  Both low and high values of k have their advantages.  The best value of k is dependent on the data.  Cross-validation can be used to compare k’s.

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7 Noise: 20 Noise: 30 Noise: 40 Noise: 50 Noise: 60 Noise: 70

8 Number of points of random noise (for each colour) Percentage of unsuccessfully tested red points Percentage of unsuccessfully tested blue points 2013%18% 3027%24% 4033%20% 5030%24% 6030%26% 7035%30% Pearson product- moment correlation coefficient:

9 Condensed nearest neighbour data reduction method

10  Outliers are points whose k nearest points are not of the same class.  X={x 1, x 2,..., x n } (without outliers)  P={x 1 }  We scan all elements of X and move individual elements to P if their nearest prototype (their nearest element from P) has a different class label.  Repeat until no more new prototypes are found.  Absorbed points are the points which are not prototypes.

11  In the applet:  Prototypes are denoted with squares.  Outliers are denoted with crosses.  Absorbed points are denoted with empty circles.  Absorbed points and outliers are not used for classification, any maps that are created or any type of testing.

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13  The k-nearest neighbour algorithm classifies objects based on a majority vote of the k nearest training examples.  We assign the class label which is the most frequent amongst the k training examples which are nearest or most similar to our previously unseen instance.

14  CNN reduces the amount of data necessary for classification.  Points are labelled as either prototypes, outliers or absorbed points.  Absorbed points and outliers are then no longer used in classification tasks, validation tests or maps of the data set.

15  Adam McMaster, et al, 2011, Wikipedia-k-Nearest-Neighbor-Algorithm. [pdf] Available at: http://www.saylor.org/site/wp-content/uploads/2011/02/Wikipedia-k- Nearest-Neighbor-Algorithm.pdf [Accessed 12 Feb 2012]http://www.saylor.org/site/wp-content/uploads/2011/02/Wikipedia-k- Nearest-Neighbor-Algorithm.pdf  E.M. Mirkes, University of Leicester, 2011, kNN and Potential Energy [online] Available at: [Accessed 2 Feb 2012]http://www.math.le.ac.uk/people/ag153/homepage/KNN/KNN3.html  Antal van den Bosch, 2007, K-nearest neighbor classification [video online] Available at: http://videolectures.net/aaai07_bosch_knnc/ [Accessed 10 Feb 2012]http://videolectures.net/aaai07_bosch_knnc/  David Claus, 2004, Nearest Neighbour Condensing and Editing [ppt] Available at: www.robots.ox.ac.uk/~dclaus/cameraloc/samples/nearestneighbour.ppt [Accessed 12 Feb 2012] www.robots.ox.ac.uk/~dclaus/cameraloc/samples/nearestneighbour.ppt  mathematicalmonk, 2011, (ML 1.6) k-Nearest Neighbor classification algorithm [video online] Available at: http://www.youtube.com/watch?v=4ObVzTuFivY [Accessed 10 Feb 2012]http://www.youtube.com/watch?v=4ObVzTuFivY  László Kozma, 2008, k Nearest Neighbors algorithm (kNN) [pdf] Available at: http://www.lkozma.net/knn2.pdf [Accessed 12 Feb 2012] http://www.lkozma.net/knn2.pdf  Ola Söder, 2008, kNN classifiers 1. What is a kNN classifier? [online] Available at: [Accessed 12 Feb 2012] http://www.fon.hum.uva.nl/praat/manual/kNN_classifiers_1__What_is_a_kNN_cla ssifier_.html

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