The Experts below are selected from a list of 219624 Experts worldwide ranked by ideXlab platform
Nada Lavrač - One of the best experts on this subject based on the ideXlab platform.
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Supervised descriptive rule discovery : a unifying survey of Contrast Set, emerging pattern and subgroup mining
Journal of machine learning research, 2016Co-Authors: Nada Lavrač, Petra Kralj Novak, Geoffrey I. WebbAbstract:This paper gives a survey of Contrast Set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subSets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.
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CSM-SD: Methodology for Contrast Set mining through subgroup discovery
Journal of biomedical informatics, 2008Co-Authors: Petra Kralj Novak, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:This paper addresses a data analysis task, known as Contrast Set mining, whose goal is to find differences between Contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning Settings, a one-versus-all and a pairwise Contrast Set mining Setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered Contrast Sets can be improved by offering additional Contrast Set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.
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Contrast Set mining for distinguishing between similar diseases
Artificial Intelligence in Medicine in Europe, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstacicAbstract:The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of Contrast Set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
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Contrast Set mining through subgroup discovery applied to brain ischaemina data
Knowledge Discovery and Data Mining, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstacicAbstract:Contrast Set mining aims at finding differences between different groups. This paper shows that a Contrast Set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to Contrast Set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in Contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.
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AIME - Contrast Set Mining for Distinguishing Between Similar Diseases
Artificial Intelligence in Medicine, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of Contrast Set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
Antonija Krstačić - One of the best experts on this subject based on the ideXlab platform.
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CSM-SD: Methodology for Contrast Set mining through subgroup discovery
Journal of biomedical informatics, 2008Co-Authors: Petra Kralj Novak, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:This paper addresses a data analysis task, known as Contrast Set mining, whose goal is to find differences between Contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning Settings, a one-versus-all and a pairwise Contrast Set mining Setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered Contrast Sets can be improved by offering additional Contrast Set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.
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AIME - Contrast Set Mining for Distinguishing Between Similar Diseases
Artificial Intelligence in Medicine, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of Contrast Set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
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PAKDD - Contrast Set mining through subgroup discovery applied to brain ischaemina data
Advances in Knowledge Discovery and Data Mining, 1Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:Contrast Set mining aims at finding differences between different groups. This paper shows that a Contrast Set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to Contrast Set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in Contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.
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Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data
11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007, 1Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:The goal of exploratory pattern mining is to find patterns that exhibit yet unknown relationships in data and to provide insightful representations of detected relationships. This paper explores Contrast Set mining and an approach to improving its explanatory potential by using the so called supporting factors that provide additional descriptions of the detected patterns. The proposed methodology is described in a medical data analysis problem of distinguishing between similar diseases in the analysis of patients suffering from brain ischaemia.
Dragan Gamberger - One of the best experts on this subject based on the ideXlab platform.
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CSM-SD: Methodology for Contrast Set mining through subgroup discovery
Journal of biomedical informatics, 2008Co-Authors: Petra Kralj Novak, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:This paper addresses a data analysis task, known as Contrast Set mining, whose goal is to find differences between Contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning Settings, a one-versus-all and a pairwise Contrast Set mining Setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered Contrast Sets can be improved by offering additional Contrast Set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.
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Contrast Set mining for distinguishing between similar diseases
Artificial Intelligence in Medicine in Europe, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstacicAbstract:The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of Contrast Set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
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Contrast Set mining through subgroup discovery applied to brain ischaemina data
Knowledge Discovery and Data Mining, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstacicAbstract:Contrast Set mining aims at finding differences between different groups. This paper shows that a Contrast Set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to Contrast Set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in Contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.
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AIME - Contrast Set Mining for Distinguishing Between Similar Diseases
Artificial Intelligence in Medicine, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of Contrast Set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
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PAKDD - Contrast Set mining through subgroup discovery applied to brain ischaemina data
Advances in Knowledge Discovery and Data Mining, 1Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:Contrast Set mining aims at finding differences between different groups. This paper shows that a Contrast Set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to Contrast Set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in Contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.
Eamonn Keogh - One of the best experts on this subject based on the ideXlab platform.
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Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile
Data Mining and Knowledge Discovery, 2018Co-Authors: Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Zachary Zimmerman, Diego Furtado Silva, Abdullah Mueen, Eamonn KeoghAbstract:The last decade has seen a flurry of research on all-pairs-similarity-search (or similarity joins ) for text, DNA and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. However, there has been surprisingly little progress made on similarity joins for time series subsequences . The lack of progress probably stems from the daunting nature of the problem. For even modest sized dataSets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality pruning and early abandoning) at best produce only one or two orders of magnitude speedup. In this work we introduce a novel scalable algorithm for time series subsequence all-pairs-similarity-search. For exceptionally large dataSets, the algorithm can be trivially cast as an anytime algorithm and produce high-quality approximate solutions in reasonable time and/or be accelerated by a trivial porting to a GPU framework. The exact similarity join algorithm computes the answer to the time series motif and time series discord problem as a side-effect, and our algorithm incidentally provides the fastest known algorithm for both these extensively-studied problems. We demonstrate the utility of our ideas for many time series data mining problems, including motif discovery, novelty discovery, shapelet discovery, semantic segmentation, density estimation, and Contrast Set mining. Moreover, we demonstrate the utility of our ideas on domains as diverse as seismology, music processing, bioinformatics, human activity monitoring, electrical power-demand monitoring and medicine.
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PKDD - Group SAX: extending the notion of Contrast Sets to time series and multimedia data
Lecture Notes in Computer Science, 2006Co-Authors: Jessica Lin, Eamonn KeoghAbstract:In this work, we take the traditional notation of Contrast Sets and extend them to other data types, in particular time series and by extension, images. In the traditional sense, Contrast-Set mining identifies attributes, values and instances that differ significantly across groups, and helps user understand the differences between groups of data. We reformulate the notion of Contrast-Sets for time series data, and define it to be the key pattern(s) that are maximally different from the other Set of data. We propose a fast and exact algorithm to find the Contrast Sets, and demonstrate its utility in several diverse domains, ranging from industrial to anthropology. We show that our algorithm achieves 3 orders of magnitude speedup from the brute-force algorithm, while producing exact solutions.
Petra Kralj - One of the best experts on this subject based on the ideXlab platform.
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Contrast Set mining for distinguishing between similar diseases
Artificial Intelligence in Medicine in Europe, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstacicAbstract:The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of Contrast Set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
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Contrast Set mining through subgroup discovery applied to brain ischaemina data
Knowledge Discovery and Data Mining, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstacicAbstract:Contrast Set mining aims at finding differences between different groups. This paper shows that a Contrast Set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to Contrast Set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in Contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.
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AIME - Contrast Set Mining for Distinguishing Between Similar Diseases
Artificial Intelligence in Medicine, 2007Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of Contrast Set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
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PAKDD - Contrast Set mining through subgroup discovery applied to brain ischaemina data
Advances in Knowledge Discovery and Data Mining, 1Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:Contrast Set mining aims at finding differences between different groups. This paper shows that a Contrast Set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to Contrast Set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in Contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.
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Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data
11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007, 1Co-Authors: Petra Kralj, Nada Lavrač, Dragan Gamberger, Antonija KrstačićAbstract:The goal of exploratory pattern mining is to find patterns that exhibit yet unknown relationships in data and to provide insightful representations of detected relationships. This paper explores Contrast Set mining and an approach to improving its explanatory potential by using the so called supporting factors that provide additional descriptions of the detected patterns. The proposed methodology is described in a medical data analysis problem of distinguishing between similar diseases in the analysis of patients suffering from brain ischaemia.