Prepared by: Mahmoud Rafeek Al-Farra College of Science & Technology Dep. Of Computer Science & IT BCs of Information Technology Data Mining Chapter 4_3: Classification Methods (Examples) Prepared by: Mahmoud Rafeek Al-Farra 2013 www.cst.ps/staff/mfarra

Course’s Out Lines Introduction Data Preparation and Preprocessing Data Representation Classification Methods Evaluation Clustering Methods Mid Exam Association Rules Knowledge Representation Special Case study : Document clustering Discussion of Case studies by students

Out Lines Machine learning techniques k-Nearest Neighbors Naïve Bayesian Classifiers

k-Nearest Neighbors Also called instance based learning. K-nearest neighbor is a supervised learning algorithm where the result of new instance query is classified based on majority of K-nearest neighbor category. The purpose of this algorithm is to classify a new object based on attributes and training samples.

k-Nearest Neighbors The classifiers do not use any model to fit and only based on memory. Given a query point, We find K number of objects or (training points) closest to the query point. The classification is using majority vote among the classification of the K objects.

k-Nearest Neighbors - Algorithm Given a new instance x, find its nearest neighbor <x’,y’> Return y’ as the class of x

Advantage and Disadvantage Robust to noisy training data Effective if the training data is large Disadvantage Need to determine value of parameter K Distance based learning is not clear which type of distance to use and which attribute to use to produce the best results. Computation cost is quite high because we need to compute distance of each query instance to all training samples.

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