On Subgroup Discovery in Numerical Domains

Großkreutz, Henrik; Rüping, Stefan

doi:10.1007/978-3-642-04180-8_15

Cited by 23 publications

(35 citation statements)

References 1 publication

(1 reference statement)

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“…Such restrictions usually compel the attribute domain to be nominal [1], [2], or impose an anti-monotonicity property on the quality measure which is then used to prune the search space [14]. We choose to free this paper from such restrictions, but notice that this is by no means essential to the described method.…”

Section: Definition (Subgroup)mentioning

confidence: 99%

Exploiting False Discoveries -- Statistical Validation of Patterns and Quality Measures in Subgroup Discovery

Duivesteijn

Knobbe

2011

2011 IEEE 11th International Conference on Data Mining

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Subgroup discovery suffers from the multiple comparisons problem: we search through a large space, hence whenever we report a set of discoveries, this set will generally contain false discoveries. We propose a method to compare subgroups found through subgroup discovery with a statistical model we build for these false discoveries. We determine how much the subgroups we find deviate from the model, and hence statistically validate the found subgroups. Furthermore we propose to use this subgroup validation to objectively compare quality measures used in subgroup discovery, by determining how much the top subgroups we find with each measure deviate from the statistical model generated with that measure. We thus aim to determine how good individual measures are in selecting significant findings. We invoke our method to experimentally compare popular quality measures in several subgroup discovery settings.

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Section: Definition (Subgroup)mentioning

confidence: 99%

Exploiting False Discoveries -- Statistical Validation of Patterns and Quality Measures in Subgroup Discovery

Duivesteijn

Knobbe

2011

2011 IEEE 11th International Conference on Data Mining

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“…(In fact, if statistical dependency were a monotonic property, correlated sets would capture also dependency rules, unless other extra heuristics were used.) In the context of classification rule search, there is even a globally optimal algorithm [16], which can handle small data sets, as long as the rule complexity is restricted.…”

Section: Handling Numerical and Periodic Variablesmentioning

confidence: 99%

Thorough Analysis of Log Data with Dependency Rules: Practical Solutions and Theoretical Challenges

Hämäläinen

2012

2012 IEEE 12th International Conference on Data Mining Workshops

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In this paper, we present our vision how statistical dependency rule mining could be applied to a thorough analysis of log data. Dependency rules are especially attractive as a first step mining method due to their efficient algorithms and globally optimal results. The major drawback is a rather specific form of the dependencies, which requires binary data. It is not always clear how heterogeneous real world data should be binarized and how the tools should be used so that all interesting dependencies would be caught.We give an overview of typical problems when analyzing log data. The three major problems are: 1) How to balance between groups and individuals such that both general regularities and individual peculiarities can be found? 2) How to handle numerical and periodic variables? 3) How to extract features from the intrinsic dimensions of log data? For each problem, we give practical solutions in the form of preprocessing techniques and constraints which can be used with the existing tools. We also point out important research problems and algorithmic challenges, which would require further research.

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“…Quality constraint is also one of the widely used pruning strategies for some subgroup discovery methods [22][23] . These methods prune the candidates that have a lower quality than a user-specified threshold value.…”

Section: Pruningmentioning

confidence: 99%

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

Helal

2016

J. Comput. Sci. Technol.

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Subgroup discovery is a data mining technique that discovers interesting associations among different variables with respect to a property of interest. Existing subgroup discovery methods employ different strategies for searching, pruning and ranking subgroups. It is very crucial to learn which features of a subgroup discovery algorithm should be considered for generating quality subgroups. In this regard, a number of reviews have been conducted on subgroup discovery. Although they provide a broad overview on some popular subgroup discovery methods, they employ few datasets and measures for subgroup evaluation. In the light of the existing measures, the subgroups cannot be appraised from all perspectives. Our work performs an extensive analysis on some popular subgroup discovery methods by using a wide range of datasets and by defining new measures for subgroup evaluation. The analysis result will help with understanding the major subgroup discovery methods, uncovering the gaps for further improvement and selecting the suitable category of algorithms for specific application domains.

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On Subgroup Discovery in Numerical Domains

Cited by 23 publications

References 1 publication

Exploiting False Discoveries -- Statistical Validation of Patterns and Quality Measures in Subgroup Discovery

Exploiting False Discoveries -- Statistical Validation of Patterns and Quality Measures in Subgroup Discovery

Thorough Analysis of Log Data with Dependency Rules: Practical Solutions and Theoretical Challenges

Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

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