An overview on subgroup discovery: foundations and applications

Herrera, F.; Carmona, C. J.; González, Pedro; Jesus, María José del

doi:10.1007/s10115-010-0356-2

Cited by 241 publications

(151 citation statements)

References 78 publications

Supporting

Mentioning

147

Contrasting

Unclassified

Order By: Relevance

“…Similar results can be also obtained by data mining techniques: One is the well-known subgroup discovery [17], whose aim is discovering interesting relationships between different properties (attributes) of a set with respect to a target variable.The patterns extracted are normally represented in the form of rules and called subgroups. Subgroup discovery is mainly concerned with categorical attributes, while the use of distributions, or of entropy, allows also for numerical attributes; moreover, the adoption of these two typical statistical techniques also allows for the use of traditional statistical hypothesis tests.…”

Section: A Database Perspectivesupporting

confidence: 54%

“…Another interesting data mining technique is subgroup discovery [17] that considers a set of items characterized by some properties, one of which being the target one. The aim is to identify the subgroups of the population that have the most unusual features with respect to the target property, and their descriptions, in most cases expressed through rules.…”

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Exploratory computing: a comprehensive approach to data sensemaking

Blas

Mazuran

Puppa

et al. 2016

Int J Data Sci Anal

View full text Add to dashboard Cite

The Big Data challenge has made the issue of "making sense" of data urgent and unavoidable. This paper introduces exploratory computing (EC), a novel paradigm whose aim is to support a comprehensive "exploratory" experience for the user. "Exploratory" because it supports search and discovery of information through various tasks (investigation, knowledge seeking, serendipitous discovery, comparison of information…) in a dynamic interaction, where meaningful feedbacks from the system play a crucial role, closely resembling a human-to-human dialogue. "Computing" because a complex interaction as the one outlined above requires powerful computational strength for the user to be able to fully profit from, and even enjoy, the interaction. EC is not associated with a predefined set of techniques: Rather, it is an approach that can be concretized in different ways. In the paper, two different implementations of the EC approach are presented, both of which interpret the EC highlevel requirements. It is the authors' hope that others will follow.

show abstract

Section: A Database Perspectivesupporting

confidence: 54%

Section: State Of the Artmentioning

confidence: 99%

Exploratory computing: a comprehensive approach to data sensemaking

Blas

Mazuran

Puppa

et al. 2016

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…Subgroup Discovery (SD) aims to find subgroups of data that are statistically different given a property of interest [30,58,59,21]. SD lies between …”

Section: Subgroup Discoverymentioning

confidence: 99%

Searching for rules to detect defective modules: A subgroup discovery approach

Rodríguez

Ruíz

Riquelme

et al. 2012

Information Sciences

View full text Add to dashboard Cite

Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsistency, redundancy) not all classification algorithms are capable of dealing with this task conveniently. To deal with these problems, Subgroup Discovery (SD) algorithms can be used to find groups of statistically different data given a property of interest. We propose EDER-SD (Evolutionary Decision Rules for Subgroup Discovery), a SD algorithm based on evolutionary computation that induces rules describing only faultprone modules. The rules are a well-known model representation that can be easily understood and applied by project managers and quality engineers. Thus, rules can help them to develop software systems that can be justifiably trusted. Contrary to other approaches in SD, our algorithm has the advantage of working with continuous variables as the conditions of the rules are defined using intervals. We describe the rules obtained by applying our algorithm to seven publicly available datasets from the PROMISE repository * Corresponding author Email addresses: daniel.rodriguezg@uah.es (D. Rodríguez), robertoruiz@upo.es (R. Ruiz), riquelme@lsi.us.es (J.C. Riquelme), aguilar@upo.es (J.S. Aguilar-Ruiz) February 4, 2011 showing that they are capable of characterising subgroups of fault-prone modules. We also compare our results with three other well known SD algorithms and the EDER-SD algorithm performs well in most cases. Preprint submitted to Information Sciences

show abstract

“…The model consists of descriptions for positive, negative and noisy examples. Each description is parameterized by a coverage measure [30]. To keep up-to-date knowledge, the explicit forgetting is used to remove older examples and the implicit forgetting to change the coverage measure values.…”

Section: Related Workmentioning

confidence: 99%

Decision rules extraction from data stream in the presence of changing context for diabetes treatment

Tomczak

Gonczarek

2012

Knowl Inf Syst

View full text Add to dashboard Cite

The knowledge extraction is an important element of the e-Health system. In this paper, we introduce a new method for decision rules extraction called Graph-based Rules Inducer to support the medical interview in the diabetes treatment. The emphasis is put on the capability of hidden context change tracking. The context is understood as a set of all factors affecting patient condition. In order to follow context changes, a forgetting mechanism with a forgetting factor is implemented in the proposed algorithm. Moreover, to aggregate data, a graph representation is used and a limitation of the search space is proposed to protect from overfitting. We demonstrate the advantages of our approach in comparison with other methods through an empirical study on the Electricity benchmark data set in the classification task. Subsequently, our method is applied in the diabetes treatment as a tool supporting medical interviews.

show abstract

An overview on subgroup discovery: foundations and applications

Cited by 241 publications

References 78 publications

Exploratory computing: a comprehensive approach to data sensemaking

Exploratory computing: a comprehensive approach to data sensemaking

Searching for rules to detect defective modules: A subgroup discovery approach

Decision rules extraction from data stream in the presence of changing context for diabetes treatment

Contact Info

Product

Resources

About