Exceptional Model Mining

Abstract

Abstract. In most databases, it is possible to identify small partitions of the data where the observed distribution is notably different from that of the database as a whole. In classical subgroup discovery, one considers the distribution of a single nominal attribute, and exceptional subgroups show a surprising increase in the occurrence of one of its values. In this paper, we introduce Exceptional Model Mining (EMM), a framework that allows for more complicated target concepts. Rather than finding subgroups based on the distribution of a single target attribute, EMM finds subgroups where a model fitted to that subgroup is somehow exceptional. We discuss regression as well as classification models, and define quality measures that determine how exceptional a given model on a subgroup is. Our framework is general enough to be applied to many types of models, even from other paradigms such as association analysis and graphical modeling.

Discovering a taste for the unusual: exceptional models for preference mining

“…. , t are available, and if we are not interested in finding unusual target distributions, but unusual target interactions, we can employ Exceptional Model Mining (EMM) (Duivesteijn 2013;Duivesteijn et al 2016) instead of SD. EMM is instantiated by selecting two things: a model class and a quality measure.…”

“…Typically, subgroups are found by a level-wise search through attribute space (Duivesteijn 2013). However, we consider the exact search strategy to be a parameter of the algorithm.…”

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

“…The algorithms were implemented in Python. The experiments were carried on an Intel Core i7-6700HQ 2.60 GHz machine with 16 GB RAM and were run by PyPy 5.4.1 For reproducibility purpose, the source code and the data are made available in our companion page 7 . These experiments aim to answer the following questions: Q1 -Is the closure over an HMT attribute more effective than mining closed itemsets?…”

“…The more different is the model, the more exceptional is the subgroup. Many models have been investigated in the last decade [21,8,7,13,6]. However, no model in the EMM framework makes it possible to characterize collection of individuals whose pairwise agreement exceptionally deviates according to a subset of objects.…”

Subgroup Discovery with Proper Scoring Rules

“…In [4,11] different model classes. For example, if linear regression models are trained on the whole data set and different candidate subgroups, the quality of subgroups can be evaluated by comparing the regression coefficient between the global model and the local subgroup model.…”