Systematic Construction of Anomaly Detection Benchmarks from
... vary the set of features to manipulate both the power of the relevant features and the number of irrelevant or “noise” features. ...
... vary the set of features to manipulate both the power of the relevant features and the number of irrelevant or “noise” features. ...
Medical Data Mining Techniques for Health Care Systems
... with 0.7919 correlation coefficients has the least accuracy. The work of Hariganesh S et al [6] discusses the Parkinson’s disease of remote tracking used by eleven techniques with 4406 training dataset and 1469 data of test set. The elevated accuracy of correlation coefficient in dataset is 99.85% a ...
... with 0.7919 correlation coefficients has the least accuracy. The work of Hariganesh S et al [6] discusses the Parkinson’s disease of remote tracking used by eleven techniques with 4406 training dataset and 1469 data of test set. The elevated accuracy of correlation coefficient in dataset is 99.85% a ...
Coactive Learning for Distributed Data Mining
... Instance-Based Learning Instance-based learning (IBL) is an inductive learning model, that generates concept descriptions by storing specific training instances (Aha, Kibler, and Albert 1991). This approach improves prior work on nearest neighbor classification (Cover and Hart 1967) which stores all ...
... Instance-Based Learning Instance-based learning (IBL) is an inductive learning model, that generates concept descriptions by storing specific training instances (Aha, Kibler, and Albert 1991). This approach improves prior work on nearest neighbor classification (Cover and Hart 1967) which stores all ...
Research on High-Dimensional Data Reduction
... (1) In the treatment of single view data, CCA back to the linear discriminant to analyze the effect, which inevitably has the inherent defects of the latter, such as the problem of the small sample size and distribution of the data dependence, the dimension constraint of the dimensionality reduction ...
... (1) In the treatment of single view data, CCA back to the linear discriminant to analyze the effect, which inevitably has the inherent defects of the latter, such as the problem of the small sample size and distribution of the data dependence, the dimension constraint of the dimensionality reduction ...
Classifier Ensembles for Detecting Concept Change in Streaming
... p trading standards, etc.) Using a threshold of f σ, p(1 − p)/N , a change is detected if p̂ > p + f σ. where σ = This model is known as Shewhart control chart, or also p-chart when binary data is being monitored. The typical value of f is 3, but many other alternative and compound criteria have bee ...
... p trading standards, etc.) Using a threshold of f σ, p(1 − p)/N , a change is detected if p̂ > p + f σ. where σ = This model is known as Shewhart control chart, or also p-chart when binary data is being monitored. The typical value of f is 3, but many other alternative and compound criteria have bee ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... some experimental data to sustain this comparison a representative algorithm from both categories mentioned above was chosen (the Apriori, FP-growth and DynFP-growth algorithms). The compared algorithms are presented together with some experimental data that lead to the final conclusions. Also, the ...
... some experimental data to sustain this comparison a representative algorithm from both categories mentioned above was chosen (the Apriori, FP-growth and DynFP-growth algorithms). The compared algorithms are presented together with some experimental data that lead to the final conclusions. Also, the ...
High Performance Data mining by Genetic Neural Network
... fact, the use of a validation set can detect any irregularities in the data and prevents the optimal weights for the network. Balance between genetic programming and neural networks, the network topology are an interesting topic. In advance of his generation program using appropriate structure for t ...
... fact, the use of a validation set can detect any irregularities in the data and prevents the optimal weights for the network. Balance between genetic programming and neural networks, the network topology are an interesting topic. In advance of his generation program using appropriate structure for t ...
A New Class Based Associative Classification Algorithm
... Classification is one of the most important tasks in data mining. Researchers are focusing on designing classification algorithms to build accurate and efficient classifiers for large data sets. Being a new classification method that integrates association rule mining into classification problems, a ...
... Classification is one of the most important tasks in data mining. Researchers are focusing on designing classification algorithms to build accurate and efficient classifiers for large data sets. Being a new classification method that integrates association rule mining into classification problems, a ...
Extraction of Best Attribute Subset using Kruskal`s Algorithm
... diagram that is much longer/shorter than its neighbours. The result is a backwoods and every tree forest represents a cluster. In our study, we apply graph theoretic clustering methods to attributes. Specifically, we embrace the minimum spanning tree(MST) based grouping algorithms. An attribute sele ...
... diagram that is much longer/shorter than its neighbours. The result is a backwoods and every tree forest represents a cluster. In our study, we apply graph theoretic clustering methods to attributes. Specifically, we embrace the minimum spanning tree(MST) based grouping algorithms. An attribute sele ...
Multi-Link Lists as Data Cube Structure in the MOLAP Environment
... and fixed-sized array that would require 2,972,200 cells (10*14*11*1930), which corresponds to the size of the MDA. Whatever the number of tuples taken in the performance survey, the array size is limited by 2,972,200 * (the size of a cell). This fixed-sized array may be sparse. Indeed, if we increa ...
... and fixed-sized array that would require 2,972,200 cells (10*14*11*1930), which corresponds to the size of the MDA. Whatever the number of tuples taken in the performance survey, the array size is limited by 2,972,200 * (the size of a cell). This fixed-sized array may be sparse. Indeed, if we increa ...
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.