Subgroup discovery in data sets with multi-dimensional responses (original) (raw)
Related papers
2009
Biomedical experimental data sets may often include many features both at input (description of cases, treatments, or experimental parameters) and output (outcome description). State-of-the-art data mining techniques can deal with such data, but would consider only one output feature at the time, disregarding any dependencies among them. In the paper, we propose the technique that can treat many output features simultaneously, aiming at finding subgroups of cases that are similar both in input and output space. The method is based on k-medoids clustering and analysis of contingency tables, and reports on case subgroups with significant dependency in input and output space. We have used this technique in explorative analysis of clinical data on femoral neck fractures. The subgroups discovered in our study were considered meaningful by the participating domain expert, and sparked a number of ideas for hypothesis to be further experimentally tested.
2021
We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global modelling perspective. First, we formulate the broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables, and that includes traditional top-1 subgroup discovery in its definition. This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Second, as finding optimal subgroup lists is NP-hard, we propose SSD++, a greedy heuristic that finds good subgroup lists and guarantees that the most significant subgroup found according to the MDL criterion is added in each iteration, which is shown to be equivalent to a Bayesian one-sample proportions, multinomial, or t-test between the subgroup and dataset marginal target distributions plus a multiple hypothesis testing penalty. We empirically show on 54 datasets that SSD++ outperforms previous subgroup set discovery methods in terms of quality and subgroup list size.
An overview on subgroup discovery: Foundations and applications
2010
Abstract Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.
Subgroup Discovery with Proper Scoring Rules
Machine Learning and Knowledge Discovery in Databases
Subgroup Discovery is the process of finding and describing sufficiently large subsets of a given population that have unusual distributional characteristics with regard to some target attribute. Such subgroups can be used as a statistical summary which improves on the default summary of stating the overall distribution in the population. A natural way to evaluate such summaries is to quantify the difference between predicted and empirical distribution of the target. In this paper we propose to use proper scoring rules, a well-known family of evaluation measures for assessing the goodness of probability estimators, to obtain theoretically well-founded evaluation measures for subgroup discovery. From this perspective, one subgroup is better than another if it has lower divergence of target probability estimates from the actual labels on average. We demonstrate empirically on both synthetic and real-world data that this leads to higher quality statistical summaries than the existing methods based on measures such as Weighted Relative Accuracy.
Identifying and Assessing Interesting Subgroups in a Heterogeneous Population
BioMed Research International, 2015
Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such ask-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability—the basis of cluster generation—is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identificati...
Mining atypical groups for a target quantitative attribute
2008 IEEE Conference on Cybernetics and Intelligent Systems, 2008
An important task in data analysis is the understanding of unexpected or atypical behaviors in a group of individuals. Which categories of individuals earn the higher salaries or, on the contrary, which ones earn the lower salaries? We present the problem of how data concerning atypical groups can be mined compared with a target quantitative attribute, like for instance the attribute "salary", and in particular for the high and low values of a user-defined interval. Our search therefore focuses on conjunctions of attributes whose distribution differs significantly from the learning set for the interval's high and low values of the target attribute. Such atypical groups can be found by adapting an existing measure, the intensity of inclination. This measure frees us from the transformation step of quantitative attributes, that is to say the step of discretization followed by a complete disjunctive coding. Thus, we propose an algorithm for mining such groups using pruning rules in order to reduce the complexity of the problem. This algorithm has been developed and integrated into the WEKA software for knowledge extraction. Finally we give an example of data extraction from the American census database IPUMS.
Discovering outstanding subgroup lists for numeric targets using MDL
ECML PKDD 2020: Machine Learning and Knowledge Discovery in Databases , 2020
The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant subgroups, subgroup set discovery (SSD) has been proposed. State-of-the-art SSD methods have their limitations though, as they typically heavily rely on heuristics and/or user-chosen hyperparameters. We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists. We argue that the best subgroup list is the one that best summarizes the data given the overall distribution of the target. We restrict our focus to a single numeric target variable and show that our formalization coincides with an existing quality measure when finding a single subgroup, but that-in addition-it allows to trade off subgroup quality with the complexity of the subgroup. We next propose SSD++, a heuristic algorithm for which we empirically demonstrate that it returns outstanding subgroup lists: non-redundant sets of compact subgroups that stand out by having strongly deviating means and small spread.
Apriori-SD: Adapting Association Rule Learning to Subgroup Discovery
Applied Artificial Intelligence, 2006
& This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. The paper contributes to subgroup discovery, to a better understanding of the weighted covering algorithm, and the properties of the weighted relative accuracy heuristic by analyzing their performance in the ROC space. An experimental comparison with rule learners CN2, RIPPER, and APRIORI-C on UCI data sets demonstrates that APRIORI-SD produces substantially smaller rulesets, where individual rules have higher coverage and significance. APRIORI-SD is also compared to subgroup discovery algorithms CN2-SD and SubgroupMiner. The comparisons performed on U.K. traffic accident data show that APRIORI-SD is a competitive subgroup discovery algorithm. Standard rule learning algorithms are designed to construct classification and prediction rules (Michalski et al. 1986; Clark amd Niblett 1989; Cohen 1995). In addition to this area of machine learning, referred to as supervised learning or predictive induction, developments in descriptive induction have recently gained much attention, in particular association rule learning (Agrawal et al. 1993), subgroup discovery (Wrobel 1997; 2001), and other approaches to non-classificatory induction. This paper considers the task of subgroup discovery defined as follows (Wrobel 1997; 2001) given a population of individuals and a specific property of the individuals that we are interested in, find population subgroups that are statistically ''most interesting,'' e.g., are as large as possible and have the most unusual statistical (distributional) characteristics with respect to the property of interest.
REVIEW ON DATA MINING TECHNIQUES FOR SUBGROUP DISCOVERY
Subgroup discovery is a data mining technique which focuses fascinating rules regarding a target variable. A paramount feature for this method is the combination of predictive and descriptive induction. This survey gives highlights on the establishments, algorithms, and progressed studies together with the applications of subgroup discovery. This paper shows a novel data mining systems for the investigation and extraction of learning from infor mation created by electricity meters. In spite of the fact that a rich source of data for energy utilization analysis, power meters deliver a voluminous, quick paced, transient stream of information those traditional methodologies are not able to address a ltogether. So as to beat these issues, it is imperative for a data mining framework to consolidate usefulness for break summarization and incremental analysis utilizing intelligent procedures. In subgroups whose sizes are large and patterns are not usual h as to be discovered. Their models have to be generated first. The many algorithms have been used to overcome the wider range of data mining problems. This paper gives a survey on subgroup discovery patterns from smart electricity meter data.