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A Taxonomy Framework for Unsupervised Outlier Detection
A Taxonomy Framework for Unsupervised Outlier Detection

... Based on these real-life applications, it can clearly be seen that outlier detection is a quite critical part of any data analysis. In the detection of outliers, there is a universally accepted assumption that the number of anomalous data is considerably smaller than normal data in a data set. Thus, ...
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... in a group will be similar (or related) to one other and different from (or unrelated to) the objects in other groups. The greater the similarity (or homogeneity) within a group, and the greater the difference between groups, the “better” or more distinct the clustering. The definition of what const ...
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... We also assume the existence of functions add and replace, with their usual meanings, for sets and the function append for ordered sets. The nondeterministic algorithm is shown in Fig. 2 The number of query ock plans that this nondeterministic algorithm can generate is rather large. Infact, with no ...
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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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