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Multivariate Discretization for Set Mining
2001
Knowledge and Information Systems
Preserved Fulltext
similar multivariate distributions (F x ∼ F y ) across all variables and combinations of variables. ...
We present a bottom-up merging algorithm to discretize continuous variables based on this rule. ...
I thank Michael Pazzani for his support and encouragement. ...
doi:10.1007/pl00011680
fatcat:3mm3nitombhpxf4hx4kfvegkru
Mining Hierarchical Temporal Patterns in Multivariate Time Series
[chapter]
2004
Lecture Notes in Computer Science
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A semiotic hierarchy of temporal patterns, which are not a priori given, is build in a bottom up manner from static logical descriptions of multivariate time instants. ...
It defines a hierarchical temporal rule language to express complex patterns present in multivariate time series. ...
Acknowledgements We thank Niko Efthymiou, Ingo Löhken, and Michael Thies for programming the audio feature extraction. ...
doi:10.1007/978-3-540-30221-6_11
fatcat:onmic2vrmzaoheckbduvfcm5fm
Preserved right ventricular ejection fraction predicts exercise capacity and survival in advanced heart failure
1995
Journal of the American College of Cardiology
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Univariate predictors of overall survival included right ventricular ejection fraction >0.35 at rest and >0.35 at exercise and %Vo 2 _>45% (all p < 0.05). In a multivariate ...
This study was undertaken to determine which exercise and radionuclide ventriculographic variables predict prognosis in advanced heart failure. Background. ...
and Multivariate Predictors of Death and Combined End Point Death
Combined End Point
Predictor
(p value)
(p value)
Univariate
Age
0.06
0.16
Gender
0.18
0.78
Diagnosis
1.0
0.81
NYHA
0.05 ...
doi:10.1016/0735-1097(94)00511-n
pmid:7897128
fatcat:6j7jx6sngbaczegwpkj4nqkc3q
The Impact of Discretization Method on the Detection of Six Types of Anomalies in Datasets
[article]
2020
arXiv
pre-print
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This algorithm employs discretization of continuous attributes, exponentially increasing weights and discretization cut points, and a pruning heuristic to detect anomalies with an optimal number of iterations ...
To this end, experiments are conducted with various datasets and SECODA, a general-purpose algorithm for unsupervised non-parametric anomaly detection in datasets with numerical and categorical attributes ...
Instead, when a multivariate analysis is conducted, the focus will be on uncommon class values and rare combinations of (continuous and categorical) attribute values. ...
arXiv:2008.12330v1
fatcat:ghc33bwt3vhhpfsj2ogqnlamiu
Forecasting multiple functional time series in a group structure: an application to mortality
[article]
2020
arXiv
pre-print
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We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. ...
To address these issues, we introduce an extension of grouped univariate functional time series method. ...
SUPPLEMENTARY MATERIAL Code for grouped multivariate functional time-series forecasting The R code to produce point and interval forecasts from the independent and the two grouped univariate and multivariate ...
arXiv:2001.03658v1
fatcat:mz4id6rzkrevhp3ojbyia6n5rm
Is the Group Structure Important in Grouped Functional Time Series?
2021
Journal of Data Science
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Using a dynamic multivariate functional time series method, we consider joint modeling and forecasting multiple series. ...
Illustrated by Japanese sub-national age-specific mortality rates from 1975 to 2016, we investigate one- to 15-step-ahead point and interval forecast accuracies for the two group structures. ...
Acknowledgement The authors are very grateful to the editor and two reviewers for their constructive and insightful comments that lead to an improvement of the paper. ...
doi:10.6339/21-jds1031
fatcat:jjgddltccbfxlc6v5tbpz6yzhm
A review of multivariate distributions for count data derived from the Poisson distribution
2017
Wiley Interdisciplinary Reviews: Computational Statistics
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The Poisson distribution has been widely studied and used for modeling univariate count-valued data. ...
We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution ...
Overall, bringing together and comparing these diverse paradigms for probability models opens up the door for many combinations and synergies. ...
doi:10.1002/wics.1398
pmid:28983398
pmcid:PMC5624559
fatcat:p2ig6gh2vrgvrbj2farxme5mgy
Grouped Functional Time Series Forecasting: An Application to Age-Specific Mortality Rates
2016
Journal of Computational And Graphical Statistics
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However, independent forecasts of agespecific mortality rates at the sub-national levels may not add up to the forecasts at the national level. ...
To address this issue, we consider the problem of reconciling age-specific mortality rate forecasts from the viewpoint of grouped univariate time series forecasting methods (Hyndman, Ahmed, et al., 2011 ...
forecasting, bottom-up and optimal combination methods. ...
doi:10.1080/10618600.2016.1237877
fatcat:yfxxjkxkm5gjpny37lhifbcuiu
Stumping along a Summary for Exploration & Exploitation Challenge 2011
2012
Journal of machine learning research
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For the last layer, we explored several combination strategies: online bagging, exponential weighting, linear ranker, and simple averaging. ...
We exploited several kinds of trees from simple decision stumps to deep multivariate ones. ...
We would also like to thank Sylvie Tricot for providing us the Orange advertising dataset and, Carine Hue and Vincent Lemaire for their comments. ...
dblp:journals/jmlr/SalperwyckU12
fatcat:lolpzglcgrad3gg35d2crdo7fm
Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery
2007
Seventh IEEE International Conference on Data Mining (ICDM 2007)
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While many approaches to motif discovery have been developed, most are restricted to categorical data, univariate time series, or multivariate data in which the temporal patterns span all of the dimensions ...
To validate our algorithm, we discuss its theoretical properties and empirically evaluate it using several data sets including synthetic data and motion capture data collected by an on-body inertial sensor ...
For multivariate data, however, each dimension leads to its own SAX word, and so a method for combined projection is required. ...
doi:10.1109/icdm.2007.52
dblp:conf/icdm/MinnenIES07
fatcat:mix7dd5zbzaidp3kxh2kf2x24u
Combining several ordinal measures in clinical studies
2004
Statistics in Medicine
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This paper proposes the use of u-statistics for scoring multivariate ordinal data and a family of simple nonparametric tests for analysis. ...
The scoring method is demonstrated to be applicable to scoring clinical response proÿles in the treatment of psoriasis and then to identifying genomic pathways that best correlate with these proÿles. ...
Grants AI49572 and AI49832 from the National Institute of Health. ...
doi:10.1002/sim.1778
pmid:15122738
fatcat:5563jyonijdjrcrp5xkn4rpcg4
Statistical Modeling of LargeScale Scientific Simulation Data
[chapter]
2011
Next Generation of Data-Mining Applications
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Finally, AQSim's third statistical modeling technique (called multivariate clusterer) utilizes the cosine similarity measure to cluster the data into similar groups. ...
AQSim's first modeling technique (called univariate mean modeler) computes the "true" (unbiased) mean of systematic partitions of the data. ...
Bottom-up Agglomeration with a Multivariate Modeler In this section, we continue utilizing the bottom-up agglomeration algorithm of the previous section but switch from a univariate modeler to a multivariate ...
doi:10.1109/9780471696650.ch11
fatcat:cl6zpg4k5bhnfab4qqucfpaysu
Grouped functional time series forecasting: An application to age-specific mortality rates
[article]
2016
arXiv
pre-print
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However, independent forecasts at the sub-national levels may not add up to the forecasts at the national level. ...
and grouped functional time series forecasting methods. ...
, bottom-up, middle-out and optimal combination methods.Among the four, only bottom-up and optimal combination methods are suitable for forecasting a non-unique group structure. ...
arXiv:1609.04222v1
fatcat:uwtxwb72rnbernnndkxxwuquxq
A new multivariate test formulation: theory, implementation, and applications to genome-scale sequencing and expression
2016
Applied Informatics
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Located on the bottom level of this taxonomy is a conventional formulation of multivariate test, featured by a property with the weakest collegiality and a rejection with the largest p value. ...
Classically, a univariate test is considered in various case-control problems and particularly in finding susceptibility SNP in computational genomics. ...
The collegiality enhances from the bottom up towards the middle level that considers each of n different dimensions individually. ...
doi:10.1186/s40535-015-0016-4
fatcat:bazojbab7respjyihf3vpz4gdi
Unsupervised interaction-preserving discretization of multivariate data
2014
Data mining and knowledge discovery
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In particular, our method examines consecutive multivariate regions and combines them if (a) their multivariate data distributions are statistically similar, and (b) this merge reduces the MDL encoding ...
In general, but in particular for exploratory tasks, a key open question is how to discretize multivariate data such that significant associations and patterns are preserved. ...
Emmanuel Müller and Jilles Vreeken are supported by Post-Doctoral Fellowships of the Research Foundation-Flanders (fwo). ...
doi:10.1007/s10618-014-0350-5
fatcat:pwv4t4snw5djvnwd6qybqsrvjy
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