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Applications of data mining techniques in improving the
Applications of data mining techniques in improving the

... IV, IKONOS or CARTOSAT, showing a diversity of land cover forms, the traditional algorithms have not been found adequate to obtain satisfactory results, because of high variance in the data. In high resolution images, spatial neighbouring pixels are highly correlated and in the most instances, they ...
SYLLABUS COURSE TITLE STATISTICAL LEARNING
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E-Governance in Elections: Implementation of Efficient Decision
E-Governance in Elections: Implementation of Efficient Decision

... available dataset. This system assists to reduce misclassification error pruning. For each leaf in a tree, it is possible to make a number of instances which are misclassified on a training set by propagating errors from. It can be compared with error rate if the leaf was changed by the most common ...
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Poster Session 121312
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A large number of problems in data mining are related to fraud

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... Algorithms include ID3, C4.5 an Random Forests. k-Nearest Neighbor (k-NN) classifiers are very simple and computationally efficient: choose a distance metric and integer k; locate the k nearest neighbors of the training set for each member of the test set, test set point class is set to be the most ...
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K-nearest neighbors algorithm



In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.
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