![Data mining functionalities](http://s1.studyres.com/store/data/000152882_1-d5b44891c75cb01cad0f36090d40cf1b-300x300.png)
Data mining functionalities
... classification is expressed in the form of a decision tree. The decision tree, for instance, may identify price as being the single factor which best distinguishes the three classes. The tree may reveal that, after price, other features which help further distinguish objects of each class from anoth ...
... classification is expressed in the form of a decision tree. The decision tree, for instance, may identify price as being the single factor which best distinguishes the three classes. The tree may reveal that, after price, other features which help further distinguish objects of each class from anoth ...
Comparative Analysis of K-Means and Kohonen
... SOM is a clustering method. Indeed, it organizes the data in clusters (cells of map) such as the instances in the same cell are similar, and the instances in different cells are different. In this point of view, SOM gives comparable results to state-of-the-art clustering algorithm such as K-Means. T ...
... SOM is a clustering method. Indeed, it organizes the data in clusters (cells of map) such as the instances in the same cell are similar, and the instances in different cells are different. In this point of view, SOM gives comparable results to state-of-the-art clustering algorithm such as K-Means. T ...
THE OPEN SOURCE MATLAB TOOLBOX Gait
... To use the toolbox for the design of a data mining algorithm, a training data set is required. This data set is normally given by a binary Matlab project file, containing matrices and vectors with predefined structures and names. This data set is normally given by a binary Matlab project file, cont ...
... To use the toolbox for the design of a data mining algorithm, a training data set is required. This data set is normally given by a binary Matlab project file, containing matrices and vectors with predefined structures and names. This data set is normally given by a binary Matlab project file, cont ...
Chapter 7
... Sample several training sets of size n (instead of just having one training set of size n) Build a classifier for each training set Combine the classifiers’ predictions ...
... Sample several training sets of size n (instead of just having one training set of size n) Build a classifier for each training set Combine the classifiers’ predictions ...
a survey on classification and association rule mining
... Random Forests are an ensemble learning method which is used for classification and regression. Random Forests construct a number of decision trees at training time and outputting the class that is the mode of the classes output by individual trees . Random Forests Algorithm are a combination of tre ...
... Random Forests are an ensemble learning method which is used for classification and regression. Random Forests construct a number of decision trees at training time and outputting the class that is the mode of the classes output by individual trees . Random Forests Algorithm are a combination of tre ...
Boosting - UCLA Human Genetics
... T.Hastie, R.Tibshirani, J.Friedman. “The Elements of Statistical LearningData Mining,Inference, Prediction.” Springer Verlag. R. Meir and G. Rätsch. An introduction to boosting and leveraging. In S. Mendelson and A. Smola, editors, Advanced Lectures on Machine Learning, LNCS, pages 119-184. Springer ...
... T.Hastie, R.Tibshirani, J.Friedman. “The Elements of Statistical LearningData Mining,Inference, Prediction.” Springer Verlag. R. Meir and G. Rätsch. An introduction to boosting and leveraging. In S. Mendelson and A. Smola, editors, Advanced Lectures on Machine Learning, LNCS, pages 119-184. Springer ...
A New Gravitational Clustering Algorithm
... Parameters: M = 500, G = 7x10-6, ∆G = 0.01, ε = 10-4 ...
... Parameters: M = 500, G = 7x10-6, ∆G = 0.01, ε = 10-4 ...
Multivariate Time Series Classification by Combining Trend
... Trend-based and value-based approximations have been used extensively in the last decade. Kontaki et al. [10] propose using PLA to transform the time series to a vector of symbols (U and D) denoting the trend of the series. Keogh and Pazzani [8] suggest a representation that consists of piecewise li ...
... Trend-based and value-based approximations have been used extensively in the last decade. Kontaki et al. [10] propose using PLA to transform the time series to a vector of symbols (U and D) denoting the trend of the series. Keogh and Pazzani [8] suggest a representation that consists of piecewise li ...
Abstract - Bioscience Biotechnology Research Communications
... is a data mining technique used to predict group membership for data instances from instances described by a set of attributes and a class label data mining remains the hope for revealing patterns that underlie it (Witten et al., 2011; Li et al., 2016 and Kumar et al., 2016). There are some basic te ...
... is a data mining technique used to predict group membership for data instances from instances described by a set of attributes and a class label data mining remains the hope for revealing patterns that underlie it (Witten et al., 2011; Li et al., 2016 and Kumar et al., 2016). There are some basic te ...
Data Mining - Motivation - Knowledge Engineering Group
... only prior information (from knowledge about the data) and information about the distribution of the training data ...
... only prior information (from knowledge about the data) and information about the distribution of the training data ...
The Program Life Cycle. - Concordia University Wisconsin
... C# facilitates data abstraction with the .NET framework. This is extra code which makes programming with complex data easier, e.g. there are ArrayList and StringBuilder classes that simplify working with linear lists and strings, by hiding details of implementation and providing simple, ...
... C# facilitates data abstraction with the .NET framework. This is extra code which makes programming with complex data easier, e.g. there are ArrayList and StringBuilder classes that simplify working with linear lists and strings, by hiding details of implementation and providing simple, ...
Document
... P(D) - Prior probability of the data P(D|h) - Probability “likelihood” of data given the hypothesis P(h|D) = P(D|h)P(h)/P(D) Bayes Rule P(h|D) increases with P(D|h) and P(h). In learning to discover the best h given a particular D, P(D) is the same in all cases and thus is not needed. Good approach ...
... P(D) - Prior probability of the data P(D|h) - Probability “likelihood” of data given the hypothesis P(h|D) = P(D|h)P(h)/P(D) Bayes Rule P(h|D) increases with P(D|h) and P(h). In learning to discover the best h given a particular D, P(D) is the same in all cases and thus is not needed. Good approach ...
A Survey on Ensemble Methods for High Dimensional Data
... the weights of correctly classified tuples. There was a need to improve the performance of boosting because of the two reasons [5]. They are as i) it generates an assumption such that it can merge training sets with large error and results in those with small error ii) divergence reduction. Hence, t ...
... the weights of correctly classified tuples. There was a need to improve the performance of boosting because of the two reasons [5]. They are as i) it generates an assumption such that it can merge training sets with large error and results in those with small error ii) divergence reduction. Hence, t ...
Using support vector machines in predicting and classifying factors
... factors. In the classical approaches, making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. However, most of the times enough information about the probability distribution of studied variables is not available to the resea ...
... factors. In the classical approaches, making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. However, most of the times enough information about the probability distribution of studied variables is not available to the resea ...
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.