Smartphone Sensor Data Mining for Gait Abnormality Detection
... and data mining techniques in order to extract patterns that are indicative of abnormal gait. These patterns form the basis of a model. Finally, the generated models were assessed and analyzed. Their performance is indicative of the potential for a model that can detect abnormal gait, and the patte ...
... and data mining techniques in order to extract patterns that are indicative of abnormal gait. These patterns form the basis of a model. Finally, the generated models were assessed and analyzed. Their performance is indicative of the potential for a model that can detect abnormal gait, and the patte ...
Searching and Mining Trillions of Time Series Subsequences
... • There Exists Data Mining Problems that we are Willing to Wait Some Hours to Answer – a team of entomologists has spent three years gathering 0.2 trillion datapoints – astronomers have spent billions dollars to launch a satellite to collect one trillion datapoints of star-light curve data per day – ...
... • There Exists Data Mining Problems that we are Willing to Wait Some Hours to Answer – a team of entomologists has spent three years gathering 0.2 trillion datapoints – astronomers have spent billions dollars to launch a satellite to collect one trillion datapoints of star-light curve data per day – ...
Music Classification Using Significant Repeating Patterns
... the sum of frequencies for each SRP contained in m to compute the importance of x with respect to m, which is called the support and denoted by Sup(x,m). Moreover, for SRP x in class C, we sum up its support in every music piece belonging to C to compute its importance with respect to C, which is ca ...
... the sum of frequencies for each SRP contained in m to compute the importance of x with respect to m, which is called the support and denoted by Sup(x,m). Moreover, for SRP x in class C, we sum up its support in every music piece belonging to C to compute its importance with respect to C, which is ca ...
Music Classification Using Significant Repeating Patterns
... the sum of frequencies for each SRP contained in m to compute the importance of x with respect to m, which is called the support and denoted by Sup(x,m). Moreover, for SRP x in class C, we sum up its support in every music piece belonging to C to compute its importance with respect to C, which is ca ...
... the sum of frequencies for each SRP contained in m to compute the importance of x with respect to m, which is called the support and denoted by Sup(x,m). Moreover, for SRP x in class C, we sum up its support in every music piece belonging to C to compute its importance with respect to C, which is ca ...
Online outlier detection over data streams
... Comparison of different a (Alpha) values ................................................... 39 Comparison of different (3 (Beta) values ...................................................... 39 Comparison of different K values ................................................................ 40 Comp ...
... Comparison of different a (Alpha) values ................................................... 39 Comparison of different (3 (Beta) values ...................................................... 39 Comparison of different K values ................................................................ 40 Comp ...
DISTRIBUTED DATA MINING - University of Canberra
... First and foremost, I would like to express my sincere gratitude to my supervisory panel Professor Dharmendra Sharma and Dr Fariba Shadabi for the professional and personal guidance that go far beyond their responsibilities. It is their patient guidance, gentle encouragement and advices that led me ...
... First and foremost, I would like to express my sincere gratitude to my supervisory panel Professor Dharmendra Sharma and Dr Fariba Shadabi for the professional and personal guidance that go far beyond their responsibilities. It is their patient guidance, gentle encouragement and advices that led me ...
Data Mining Techniques in Fraud Detection
... committed fraud against insurance companies. The EFD system (Major et al. 1995) integrated the expert knowledge with statistical information to identify providers whose behavior did not fit the rule. The hot spots methodology (Williams et al. 1997) performed a three step process: i) k-means clusteri ...
... committed fraud against insurance companies. The EFD system (Major et al. 1995) integrated the expert knowledge with statistical information to identify providers whose behavior did not fit the rule. The hot spots methodology (Williams et al. 1997) performed a three step process: i) k-means clusteri ...
Time-Memory Trade-Off for Lattice Enumeration in a Ball
... has been improved by Ajtai et al. in [6] to 2O(n log log n/ log n) in polynomial-time. The LLL and BKZ algorithms use a lattice reduction technique that applies successive elementary transformations to the input basis in order to make them shorter and more orthogonal. In 2001, Ajtai, Kumar and Sivak ...
... has been improved by Ajtai et al. in [6] to 2O(n log log n/ log n) in polynomial-time. The LLL and BKZ algorithms use a lattice reduction technique that applies successive elementary transformations to the input basis in order to make them shorter and more orthogonal. In 2001, Ajtai, Kumar and Sivak ...
LN24 - WSU EECS
... Introduced in Kaufmann and Rousseeuw (1990) Implemented in statistical packages, e.g., Splus Use the single-link method and the dissimilarity matrix Merge nodes that have the least dissimilarity Go on in a non-descending fashion Eventually all nodes belong to the same cluster ...
... Introduced in Kaufmann and Rousseeuw (1990) Implemented in statistical packages, e.g., Splus Use the single-link method and the dissimilarity matrix Merge nodes that have the least dissimilarity Go on in a non-descending fashion Eventually all nodes belong to the same cluster ...
data stream mining - Department of Computer Science
... can learn highly accurate models from limited training examples. It is commonly assumed that the entire set of training data can be stored in working memory. More recently the need to process larger amounts of data has motivated the field of data mining. Ways are investigated to reduce the computati ...
... can learn highly accurate models from limited training examples. It is commonly assumed that the entire set of training data can be stored in working memory. More recently the need to process larger amounts of data has motivated the field of data mining. Ways are investigated to reduce the computati ...
Free-Sets: A Condensed Representation of Boolean Data for the
... (see Section 2.2), we need the free-sets and their supports. Definition 12. FreqFreeSup(r, σ, δ) is the set of all pairs containing a frequent free-set and its support, i.e., FreqFreeSup(r, σ, δ) = { X, Sup(r, X ) | X ∈ FreqFree(r, σ, δ)}. We can now define the -adequate representation w.r.t. the ...
... (see Section 2.2), we need the free-sets and their supports. Definition 12. FreqFreeSup(r, σ, δ) is the set of all pairs containing a frequent free-set and its support, i.e., FreqFreeSup(r, σ, δ) = { X, Sup(r, X ) | X ∈ FreqFree(r, σ, δ)}. We can now define the -adequate representation w.r.t. the ...
Enhancements on Local Outlier Detection
... In common situations, the number of outliers in any dataset is expected to be extremely small. It is highly inefficient for the LOF algorithm in [8] to compute the LOF values for all points inside a dataset. According to this observation, we introduce an adaptive algorithm called GridLOF (Grid-based ...
... In common situations, the number of outliers in any dataset is expected to be extremely small. It is highly inefficient for the LOF algorithm in [8] to compute the LOF values for all points inside a dataset. According to this observation, we introduce an adaptive algorithm called GridLOF (Grid-based ...
Contents
... Databases are rich with hidden information that can be used for intelligent decision making. Classification is a form of data analysis that extracts models describing important data classes. Such models, called classifiers, predict categorical (discrete, unordered) class labels. For example, we can ...
... Databases are rich with hidden information that can be used for intelligent decision making. Classification is a form of data analysis that extracts models describing important data classes. Such models, called classifiers, predict categorical (discrete, unordered) class labels. For example, we can ...
A Survey on Clustering Algorithms for Partitioning Method
... support vector machine (RSVM) [30] so that first fuzzy cmeans partitions data into appropriate clusters. Then, the samples with high membership values in each cluster are selected for training a multi-class RSVM classifier. Finally, the class labels of the remaining data points are predicted by the l ...
... support vector machine (RSVM) [30] so that first fuzzy cmeans partitions data into appropriate clusters. Then, the samples with high membership values in each cluster are selected for training a multi-class RSVM classifier. Finally, the class labels of the remaining data points are predicted by the l ...
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.