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• Objective: Design a Web-based software that predicts the appearance of a new link between two nodes in a social network
• Datasets: Dolphin Social Network: https://networkdata.ics.uci.edu/data.php?id=6
• Requirement: Implement the K-NN Algorithm (Section 6.9.1, page 348)
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Basic implementation of K-nearest neighbour Algorithm and the application of KNN to classify protein sequences as transmembrane beta barrel or non-transmembrane beta barrel on the basis of whole sequence amino acid composition given as input.
gaKnn(Genetic Algorithm Optimized K Nearest Neighbor Classification framework) is a frameowork for KNN optimization with a genetic algorithm. The genetic algothm used for this is JGAP (http://jgap.sourceforge.net/).
KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.
A Visual Studio .NET C++ application can perform machine learning using genetic algorithm, naive bayes, KNN, and Artificial Neural Networks (ANNs) read and processed from any standard ARFF.