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Ensemble Feature Ranking - Institute for Computing and Information
... labels are randomly flipped with probability e, and the features are perturbed by addition of a Gaussian noise with variance σ. The above generator differs from the generator proposed in [10] in several respects. [10] only considers linear target concepts, defined from a linear combination of the re ...
... labels are randomly flipped with probability e, and the features are perturbed by addition of a Gaussian noise with variance σ. The above generator differs from the generator proposed in [10] in several respects. [10] only considers linear target concepts, defined from a linear combination of the re ...
Mathematical Programming for Data Mining
... area of Data Mining and Knowledge Discovery in Databases. As transaction processing technologies were developed and became the mainstay of many business processes, great advances in addressing problems of reliable and accurate data capture were achieved. While transactional systems provide a solutio ...
... area of Data Mining and Knowledge Discovery in Databases. As transaction processing technologies were developed and became the mainstay of many business processes, great advances in addressing problems of reliable and accurate data capture were achieved. While transactional systems provide a solutio ...
Density Connected Clustering with Local Subspace Preferences
... But if different subsets of the points cluster well on different subspaces of the feature space, a global dimensionality reduction will fail. To overcome these problems of global dimensionality reduction, recent research proposed to compute subspace clusters. Subspace clustering aims at computing pai ...
... But if different subsets of the points cluster well on different subspaces of the feature space, a global dimensionality reduction will fail. To overcome these problems of global dimensionality reduction, recent research proposed to compute subspace clusters. Subspace clustering aims at computing pai ...
Constructing a Decision Tree for Graph
... DT-GBI was evaluated against a DNA dataset from UCI repository and applied to analyze a realworld hepatitis dataset which was provided by Chiba University Hospital as a part of evidence-based medicine. Since DNA data is a sequence of symbols, representing each sequence by attribute-value pairs by si ...
... DT-GBI was evaluated against a DNA dataset from UCI repository and applied to analyze a realworld hepatitis dataset which was provided by Chiba University Hospital as a part of evidence-based medicine. Since DNA data is a sequence of symbols, representing each sequence by attribute-value pairs by si ...
6 slides per page - DataBase and Data Mining Group
... Decision boundary is distorted by noise point ...
... Decision boundary is distorted by noise point ...
Data Mining Methods for Detection of New Malicious Executables
... signature-based method, we designed an automatic signature generator. Since the virus scanner that we used to label the data set had signatures for every malicious example in our data set, it was necessary to implement a similar signature-based method to compare with the data mining algorithms. Ther ...
... signature-based method, we designed an automatic signature generator. Since the virus scanner that we used to label the data set had signatures for every malicious example in our data set, it was necessary to implement a similar signature-based method to compare with the data mining algorithms. Ther ...
Constructing a Fuzzy Decision Tree by Integrating Fuzzy Sets and
... decision trees from collection of data. Due to observation error, uncertainty, and so on, many data collecting in real world are obtained in fuzzy forms. Fuzzy decision trees treat features as fuzzy variables and also yield simple decision trees. Moreover, the use of fuzzy sets is expected to deal w ...
... decision trees from collection of data. Due to observation error, uncertainty, and so on, many data collecting in real world are obtained in fuzzy forms. Fuzzy decision trees treat features as fuzzy variables and also yield simple decision trees. Moreover, the use of fuzzy sets is expected to deal w ...
A Near-Optimal Algorithm for Differentially-Private
... reduction is principal components analysis (PCA), which computes a low-rank approximation to the second moment matrix A of a set of points in Rd . The rank k of the approximation is chosen to be the intrinsic dimension of the data. We view this procedure as specifying a k-dimensional subspace of Rd ...
... reduction is principal components analysis (PCA), which computes a low-rank approximation to the second moment matrix A of a set of points in Rd . The rank k of the approximation is chosen to be the intrinsic dimension of the data. We view this procedure as specifying a k-dimensional subspace of Rd ...
Scalability_1.1
... Initially, each processor is assigned one of the numbers to be added and, at the end of the computation, one of the processors stores the sum of all the numbers. Assuming n = 16, Processors as well as numbers are labeled from 0 to 15. The sum of numbers with consecutive labels from i to j is denot ...
... Initially, each processor is assigned one of the numbers to be added and, at the end of the computation, one of the processors stores the sum of all the numbers. Assuming n = 16, Processors as well as numbers are labeled from 0 to 15. The sum of numbers with consecutive labels from i to j is denot ...
Classification - Ohio State Computer Science and Engineering
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
... model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. ...
The Apriori Algorithm - Institute for Mathematical Sciences
... The introduction of association rule mining in 1993 by Agrawal, Imielinski and Swami [?] and, in particular, the development of an efficient algorithm by Agrawal and Srikant [?] and by Mannila, Toivonen and Verkamo [?] marked a shift of the focus in the young discipline of data mining onto rules and ...
... The introduction of association rule mining in 1993 by Agrawal, Imielinski and Swami [?] and, in particular, the development of an efficient algorithm by Agrawal and Srikant [?] and by Mannila, Toivonen and Verkamo [?] marked a shift of the focus in the young discipline of data mining onto rules and ...
Resource management on Cloud systems with
... machines that can undertake the searching become commonplace, the opportunities for data mining increase. As the world grows in complexity, overwhelming us with the data it generates, data mining becomes our only hope for elucidating the patterns that underlie it. Intelligently analyzed data is a va ...
... machines that can undertake the searching become commonplace, the opportunities for data mining increase. As the world grows in complexity, overwhelming us with the data it generates, data mining becomes our only hope for elucidating the patterns that underlie it. Intelligently analyzed data is a va ...
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.