Workshop on Ubiquitous Data Mining
... In order to discover characteristic patterns in large spatiotemporal data sets, mining algorithms have to take into account spatial relations, such as topology and direction, as well as temporal relations. The increased use of devices that are capable of storing driving-related spatio-temporal infor ...
... In order to discover characteristic patterns in large spatiotemporal data sets, mining algorithms have to take into account spatial relations, such as topology and direction, as well as temporal relations. The increased use of devices that are capable of storing driving-related spatio-temporal infor ...
Classification: Alternative Techniques
... § Build classifier on each bootstrap sample, whose size n is the same as the original data set § Each item has probability (1 – 1/n)n of NOT being selected in a bootstrap composed of n items – So, each item has probability 1 – (1 – 1/n)n of being selected in a ...
... § Build classifier on each bootstrap sample, whose size n is the same as the original data set § Each item has probability (1 – 1/n)n of NOT being selected in a bootstrap composed of n items – So, each item has probability 1 – (1 – 1/n)n of being selected in a ...
Study Of Various Periodicity Detection Techniques In
... from one application to another. The Author presents a new algorithm called FLAME (Flexible and Accurate Motif Detector) is a flexible suffix tree based algorithm that can be used to find frequent patterns with a variety of definition of motif (pattern) models. FLAME is accurate, fast and scalable o ...
... from one application to another. The Author presents a new algorithm called FLAME (Flexible and Accurate Motif Detector) is a flexible suffix tree based algorithm that can be used to find frequent patterns with a variety of definition of motif (pattern) models. FLAME is accurate, fast and scalable o ...
pdf 167K
... is additionally supposed to profit from ongoing trends in hardware development. E.g. [13] suggests that the transfer rates of disk drives continue to improve much faster than the rotational delay time. As a consequence the optimum page size with respect to I/O will even increase. As we will show in ...
... is additionally supposed to profit from ongoing trends in hardware development. E.g. [13] suggests that the transfer rates of disk drives continue to improve much faster than the rotational delay time. As a consequence the optimum page size with respect to I/O will even increase. As we will show in ...
Lecture 4: kNN, Decision Trees
... • The k-Nearest Neighbors (kNN) method provides a simple approach to calculating predictions for unknown observations. • It calculates a prediction by looking at similar observations and uses some function of their response values to make the prediction, such as an average. • Like all prediction me ...
... • The k-Nearest Neighbors (kNN) method provides a simple approach to calculating predictions for unknown observations. • It calculates a prediction by looking at similar observations and uses some function of their response values to make the prediction, such as an average. • Like all prediction me ...
Distributed Query Processing Basics
... – XML - SQL mapping is very difficult – XML is not always the right language (e.g., decision support style queries) ...
... – XML - SQL mapping is very difficult – XML is not always the right language (e.g., decision support style queries) ...
csce462chapter5Part2PowerPointOldX
... of false negatives • However, it will decrease the number of false positives for these classifications • In this way, cost has been taken into account in the rules derived, reducing the ...
... of false negatives • However, it will decrease the number of false positives for these classifications • In this way, cost has been taken into account in the rules derived, reducing the ...
Chapter 4. Data Preprocessing Why preprocess the data? Data in
... MaxDiff: set bucket boundary between each pair for pairs have the β–1 largest differences ...
... MaxDiff: set bucket boundary between each pair for pairs have the β–1 largest differences ...
Multi-label Large Margin Hierarchical Perceptron
... We can divide our related work based on the characteristic of our SISC-ML algorithm. As the name suggests, SISC-ML is a semi-supervised approach, it uses subspace clustering, and most important of all, it is designed for multi-labeled data. Therefore, we have to look into the state-ofthe-art methods ...
... We can divide our related work based on the characteristic of our SISC-ML algorithm. As the name suggests, SISC-ML is a semi-supervised approach, it uses subspace clustering, and most important of all, it is designed for multi-labeled data. Therefore, we have to look into the state-ofthe-art methods ...
E-Learning Platform Usage Analysis
... virtual classroom, and digital collaboration. The usage of web applications can be measured with the use of indexes and metrics. However, in e-Learning platforms there are no appropriate indexes and metrics that would facilitate their qualitative and quantitative measurement. The purpose of this pap ...
... virtual classroom, and digital collaboration. The usage of web applications can be measured with the use of indexes and metrics. However, in e-Learning platforms there are no appropriate indexes and metrics that would facilitate their qualitative and quantitative measurement. The purpose of this pap ...
Improved Multi Threshold Birch Clustering Algorithm
... The clustering process usually can divide into the following steps: first we need to represents the objects by an appropriate form by identifying the most effective subset of the original features to use in clustering and transformations of the input features to produce new salient features; then we ...
... The clustering process usually can divide into the following steps: first we need to represents the objects by an appropriate form by identifying the most effective subset of the original features to use in clustering and transformations of the input features to produce new salient features; then we ...
Ant-based clustering: a comparative study of its relative performance
... analytical results (error rate and inter-vertex cuts) on a family of pseudo-random graphs and the corresponding values for the classical Fiduccia-Mattheyses [7] heuristic, available [15]. Additionally, the range of benchmark data sets employed has been very limited. Apart from the pseudo-random grap ...
... analytical results (error rate and inter-vertex cuts) on a family of pseudo-random graphs and the corresponding values for the classical Fiduccia-Mattheyses [7] heuristic, available [15]. Additionally, the range of benchmark data sets employed has been very limited. Apart from the pseudo-random grap ...
A Multi-clustering Fusion Algorithm
... is K-means, which is based on the square-error criterion. This algorithm is computationally efficient and yields good results if the clusters are compact, hyperspherical in shape and well separated in the feature space. Numerous attempts have been made to improve the performance of the simple K-means ...
... is K-means, which is based on the square-error criterion. This algorithm is computationally efficient and yields good results if the clusters are compact, hyperspherical in shape and well separated in the feature space. Numerous attempts have been made to improve the performance of the simple K-means ...
A Multi-Resolution Clustering Approach for Very Large Spatial
... Ng and Han introduced CLARANS (Clustering Large Applications based on RANdomaized Search) which is an improved k-medoid method [NH94]. This is the rst method that introduces clustering techniques into spatial data mining problems and overcomes most of the disadvantages of traditional clustering met ...
... Ng and Han introduced CLARANS (Clustering Large Applications based on RANdomaized Search) which is an improved k-medoid method [NH94]. This is the rst method that introduces clustering techniques into spatial data mining problems and overcomes most of the disadvantages of traditional clustering met ...
A review of associative classification mining
... Associative classification (AC) is a branch of a larger area of scientific study known as data mining. Fayyad et al. (1998) define data mining as one of the main phases in knowledge discovery from databases (KDD), which extracts useful patterns from data. AC integrates two known data mining tasks, a ...
... Associative classification (AC) is a branch of a larger area of scientific study known as data mining. Fayyad et al. (1998) define data mining as one of the main phases in knowledge discovery from databases (KDD), which extracts useful patterns from data. AC integrates two known data mining tasks, a ...
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.