Discovering Characteristic Actions from On
... discovery, then, is the unsupervised identification, modeling, and localization of each motif and its occurrences in the time series. Specifically, no knowledge of the number of motifs, their shape (i.e., model parameters, prototypical members, or representative examples), their locations in the tim ...
... discovery, then, is the unsupervised identification, modeling, and localization of each motif and its occurrences in the time series. Specifically, no knowledge of the number of motifs, their shape (i.e., model parameters, prototypical members, or representative examples), their locations in the tim ...
Diabetes: A Case Study with SAS Enterprise Miner 5.3
... a 10% BMI reduction on healthcare costs, it was important to treat the model as parsimoniously as possible and not include variables that would be highly correlated with BMI. In the children group, a dummy variable was utilized to treat the BMI effect differently for children 5 or under versus great ...
... a 10% BMI reduction on healthcare costs, it was important to treat the model as parsimoniously as possible and not include variables that would be highly correlated with BMI. In the children group, a dummy variable was utilized to treat the BMI effect differently for children 5 or under versus great ...
Stock market time series forecasting with data mining methods 1 *
... The forecasting methods used in studies fall into two categories: statistical/econometric and data mining/machine learning methods. Traditional statistical approaches include linear regression, calculation of moving average, exponential smoothing, and ARIMA, GARCH and VAR methods. These methods ret ...
... The forecasting methods used in studies fall into two categories: statistical/econometric and data mining/machine learning methods. Traditional statistical approaches include linear regression, calculation of moving average, exponential smoothing, and ARIMA, GARCH and VAR methods. These methods ret ...
Data Mining Tutorial
... • P-value is probability of Chi-square as great as that observed if independence is true. (Pr {c2>42.67} is 6.4E-11) • P-values all too small. • Logworth = -log10(p-value) = 10.19 • Best Chi-square max logworth. ...
... • P-value is probability of Chi-square as great as that observed if independence is true. (Pr {c2>42.67} is 6.4E-11) • P-values all too small. • Logworth = -log10(p-value) = 10.19 • Best Chi-square max logworth. ...
tl 004 a dual-step multi-algorithm approach for churn - PUC-SP
... With the aim of extracting a logical definition for churn, the raw data was employed for constructing the relevant features in accordance with the prior studies in this realm (Ansari, Kohavi, Mason, & Zheng, 2000; Hung, Yen, & Wang, 2006), and 5 individual interviews with telecom experts. The outcom ...
... With the aim of extracting a logical definition for churn, the raw data was employed for constructing the relevant features in accordance with the prior studies in this realm (Ansari, Kohavi, Mason, & Zheng, 2000; Hung, Yen, & Wang, 2006), and 5 individual interviews with telecom experts. The outcom ...
Parameter Reduction for Density-based Clustering of Large Data Sets
... the density-based clustering structure of the data. This method is used for interactive cluster analysis. • CHAMELEON has been found to be very effective in clustering convex shapes. However, the algorithm cannot handle outliers and needs parameter setting to work effectively. • TURN* is a brute for ...
... the density-based clustering structure of the data. This method is used for interactive cluster analysis. • CHAMELEON has been found to be very effective in clustering convex shapes. However, the algorithm cannot handle outliers and needs parameter setting to work effectively. • TURN* is a brute for ...
Unsupervised Outlier Detection Seminar of Machine
... The approach based on density outliers (LOF) seems to be the best for real-world data. But it was not tested on real-world collection (thousands of documents, tens of thousands of attributes). Plus, some factors are ad hoc (e.g. ...
... The approach based on density outliers (LOF) seems to be the best for real-world data. But it was not tested on real-world collection (thousands of documents, tens of thousands of attributes). Plus, some factors are ad hoc (e.g. ...
Document
... – Concept formation (e.g., what are patterns of genomic instability as measured by array CGH that constitute molecular subtypes of lung cancer capable of guiding development of new treatments?); – Feature construction (e.g., how can mass-spectrometry signals be decomposed into individual variables t ...
... – Concept formation (e.g., what are patterns of genomic instability as measured by array CGH that constitute molecular subtypes of lung cancer capable of guiding development of new treatments?); – Feature construction (e.g., how can mass-spectrometry signals be decomposed into individual variables t ...
Fuzzy-probabilistic logic for common sense
... Reference classes: A tall building and a tall person should not be compared by the same scale. For each reference class there would be a characteristic ξ, but it is the job of the logic to decide which ξ to use, whereas this paper focuses on the calculus of propositional truth-values. The common-sen ...
... Reference classes: A tall building and a tall person should not be compared by the same scale. For each reference class there would be a characteristic ξ, but it is the job of the logic to decide which ξ to use, whereas this paper focuses on the calculus of propositional truth-values. The common-sen ...
Different parameters - same prediction: An analysis of learning curves
... been improved using clustering approaches [20] or individualization techniques, such as learning student- and skillspecific parameters [16, 19, 24, 26] or modeling the parameters per school class [21]. The AFM is a generalized linear mixed model [2] applying a logistic regression. It is widely used ...
... been improved using clustering approaches [20] or individualization techniques, such as learning student- and skillspecific parameters [16, 19, 24, 26] or modeling the parameters per school class [21]. The AFM is a generalized linear mixed model [2] applying a logistic regression. It is widely used ...
Clustering II - CIS @ Temple University
... – Iteratively rescores the patterns against the mixture density produced by the parameter vector – The rescored patterns are used to update the parameter estimates – Patterns belong to the same cluster, if they are placed by their scores in a particular component ...
... – Iteratively rescores the patterns against the mixture density produced by the parameter vector – The rescored patterns are used to update the parameter estimates – Patterns belong to the same cluster, if they are placed by their scores in a particular component ...
Institutionen för datavetenskap Estimating Internet-scale Quality of Service Parameters for VoIP Markus Niemelä
... edges represent the alternative path where the call is relayed through a network over which the provider does have control. While the provider still has no control over edges AC and DB, it can now influence edge CD. Data for the full dotted path is not available however, as traffic has not yet been ...
... edges represent the alternative path where the call is relayed through a network over which the provider does have control. While the provider still has no control over edges AC and DB, it can now influence edge CD. Data for the full dotted path is not available however, as traffic has not yet been ...
A Model Counting Characterization of Diagnoses
... cannot be C without ^ working properly. One way to get around this is to include fault models in the system. These are constraints that explicitly describe the behavior of a component when it is not in its nominal mode (most expected mode of behavior of a component). Such a constraint in this exam ...
... cannot be C without ^ working properly. One way to get around this is to include fault models in the system. These are constraints that explicitly describe the behavior of a component when it is not in its nominal mode (most expected mode of behavior of a component). Such a constraint in this exam ...
