Periodicity Detection in Time Series Databases
... according to a period p starting from position l; that is, p;l ðT Þ ¼ el ; elþp ; elþ2p ; . . . ; elþðm1Þp ; where 0 l < p, m ¼ dðn lÞ=pe, and n is the length of T . For example, if T ¼ abcabbabcb, then 4;1 ðT Þ ¼ bbb and 3;0 ðT Þ ¼ aaab. Intuitively, the ratio of the number of occurrences o ...
... according to a period p starting from position l; that is, p;l ðT Þ ¼ el ; elþp ; elþ2p ; . . . ; elþðm1Þp ; where 0 l < p, m ¼ dðn lÞ=pe, and n is the length of T . For example, if T ¼ abcabbabcb, then 4;1 ðT Þ ¼ bbb and 3;0 ðT Þ ¼ aaab. Intuitively, the ratio of the number of occurrences o ...
Here - Wirtschaftsinformatik und Maschinelles Lernen, Universität
... Next to the plenary and semi-plenary talks, our scientific program accommodates 130 contributions, 16 of them in the LIS workshop. As expected, the lion’s share among the contributions comes from Germany, followed by Poland, but we have contributions from all over the world, stretching from Portugal ...
... Next to the plenary and semi-plenary talks, our scientific program accommodates 130 contributions, 16 of them in the LIS workshop. As expected, the lion’s share among the contributions comes from Germany, followed by Poland, but we have contributions from all over the world, stretching from Portugal ...
Bayesian Network Classifiers
... i=1 Pr(Ai |C), where α is a normalization constant. This is in fact the definition of naive Bayes commonly found in the literature (Langley et al., 1992). The problem of learning a Bayesian network can be informally stated as: Given a training set D = {u1 , . . . , uN } of instances of U, find a net ...
... i=1 Pr(Ai |C), where α is a normalization constant. This is in fact the definition of naive Bayes commonly found in the literature (Langley et al., 1992). The problem of learning a Bayesian network can be informally stated as: Given a training set D = {u1 , . . . , uN } of instances of U, find a net ...
The 2009 Knowledge Discovery in Data Competition (KDD Cup
... setting, which is typical of large-scale industrial applications. A large database was made available by the French Telecom company, Orange with tens of thousands of examples and variables. This dataset is unusual in that it has a large number of variables making the problem particularly challenging ...
... setting, which is typical of large-scale industrial applications. A large database was made available by the French Telecom company, Orange with tens of thousands of examples and variables. This dataset is unusual in that it has a large number of variables making the problem particularly challenging ...
Swarm Intelligence Algorithms for Data Clustering
... mimic such behaviors through computer simulation finally resulted into the fascinating field of SI. SI systems are typically made up of a population of simple agents (an entity capable of performing/executing certain operations) interacting locally with one another and with their environment. Althou ...
... mimic such behaviors through computer simulation finally resulted into the fascinating field of SI. SI systems are typically made up of a population of simple agents (an entity capable of performing/executing certain operations) interacting locally with one another and with their environment. Althou ...
Weka4WS: Enabling Distributed Data Mining on Grids
... evaluating the efficiency of the WSRF mechanisms and Weka4WS as methods to execute distributed data mining services ...
... evaluating the efficiency of the WSRF mechanisms and Weka4WS as methods to execute distributed data mining services ...
DOCTORAATSPROEFSCHRIFT
... principal developers. PaGaNe incorporates different types of statistical analysis methods, discretization algorithms, association rule miner, as well as classification algorithms, which all are based on the use of multi-dimensional numbered information spaces. The Lenses dataset is used as a test ex ...
... principal developers. PaGaNe incorporates different types of statistical analysis methods, discretization algorithms, association rule miner, as well as classification algorithms, which all are based on the use of multi-dimensional numbered information spaces. The Lenses dataset is used as a test ex ...
Literature Review on Feature Selection Methods for High
... subset that has lesser feature-feature correlation and higher feature-class correlation compared to other feature subsets is considered as the selected significant feature subset for the classification task. Liu & Setiono [4] proposed a feature subset-based feature selection method namely consistenc ...
... subset that has lesser feature-feature correlation and higher feature-class correlation compared to other feature subsets is considered as the selected significant feature subset for the classification task. Liu & Setiono [4] proposed a feature subset-based feature selection method namely consistenc ...
Intrusion detection using clustering
... distance is compared the result with the cluster radius threshold, if it is less than the defined threshold then the data point is added to the cluster otherwise new cluster is created with the data point and is centroid of the cluster the recalculated the until cluster center does not change. Algor ...
... distance is compared the result with the cluster radius threshold, if it is less than the defined threshold then the data point is added to the cluster otherwise new cluster is created with the data point and is centroid of the cluster the recalculated the until cluster center does not change. Algor ...
A Clustering-based Approach for Discovering Interesting Places in
... Recently [17] has introduced a new model for reasoning over trajectories, which allows powerful semantic analysis, called stops and moves. A stop is a semantically important part of a trajectory that is relevant for an application, and where the object has stayed for a minimal amount of time. For in ...
... Recently [17] has introduced a new model for reasoning over trajectories, which allows powerful semantic analysis, called stops and moves. A stop is a semantically important part of a trajectory that is relevant for an application, and where the object has stayed for a minimal amount of time. For in ...
Class Association Rule Mining Using Multi
... principal developers. PaGaNe incorporates different types of statistical analysis methods, discretization algorithms, association rule miner, as well as classification algorithms, which all are based on the use of multi-dimensional numbered information spaces. The Lenses dataset is used as a test ex ...
... principal developers. PaGaNe incorporates different types of statistical analysis methods, discretization algorithms, association rule miner, as well as classification algorithms, which all are based on the use of multi-dimensional numbered information spaces. The Lenses dataset is used as a test ex ...
An Architecture for High-Performance Privacy-Preserving
... A.4 Notations for Analysis, Sections 5.2.3 and 5.2.4 . . . . . . . . . . . . . . 148 A.5 Notations for Analysis, Sections 5.2.5 and 5.2.6 . . . . . . . . . . . . . . 148 A.6 Pseudocode Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 ...
... A.4 Notations for Analysis, Sections 5.2.3 and 5.2.4 . . . . . . . . . . . . . . 148 A.5 Notations for Analysis, Sections 5.2.5 and 5.2.6 . . . . . . . . . . . . . . 148 A.6 Pseudocode Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 ...
ISC–Intelligent Subspace Clustering, A Density Based Clustering
... Molecular Biology [13], CAD databases etc. However, when the number of measured attributes is large, it may be the case that two given groups differ at only a subset of the measured attributes, and so only a subset of the attributes are “relevant” to the clustering. In such cases, traditional cluste ...
... Molecular Biology [13], CAD databases etc. However, when the number of measured attributes is large, it may be the case that two given groups differ at only a subset of the measured attributes, and so only a subset of the attributes are “relevant” to the clustering. In such cases, traditional cluste ...
Technologies and Computational Intelligence
... http://developer.yahoo.com/blogs/hadoop/hadoopsorts-petabyte-16-25-hours-terabyte-62-422.html ...
... http://developer.yahoo.com/blogs/hadoop/hadoopsorts-petabyte-16-25-hours-terabyte-62-422.html ...
Functional Subspace Clustering with Application to Time Series
... mining literature, where researchers combine specialized distance metrics with simple clustering methods. Functional distance metrics allowing deformation date back several decades (Vintsyuk, 1968; Sakoe & Chiba, 1978). However, it has been shown only recently in the functional data analysis literat ...
... mining literature, where researchers combine specialized distance metrics with simple clustering methods. Functional distance metrics allowing deformation date back several decades (Vintsyuk, 1968; Sakoe & Chiba, 1978). However, it has been shown only recently in the functional data analysis literat ...
EBSCAN: An Entanglement-based Algorithm for Discovering Dense
... cannot be a solution to our geo-social clustering problem. Partitioning and hierarchical clustering are suitable for finding spherical clusters in a spatial database. However, a geographical cluster takes various arbitrary shapes. In contrast with partitioning and hierarchal clustering, density-base ...
... cannot be a solution to our geo-social clustering problem. Partitioning and hierarchical clustering are suitable for finding spherical clusters in a spatial database. However, a geographical cluster takes various arbitrary shapes. In contrast with partitioning and hierarchal clustering, density-base ...
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.