DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

K-Nearest Neighbours:
Classification

The http://en.wikipedia.org/wiki/K-Nearest_Neighbor_algorithmK-Nearest Neighbour algorithm. K-nearest neighbour algorithms handle missing values, are robust to outliers, and can be good predictors. They tend to only handle numeric variables, are sensitive to monotonic transformations, are not robust to irrelevant inputs, and provide models that are not easy to interpret. K-nearest neighbour classifier, relying on a distance function, is sensitive to noise and irrelevant features, because such features have the same influence on the classification as do good and highly predictive features. A solution to this is to pre-process the data to weight features so that irrelevant and redundant features have a lower weight.

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