654 research outputs found
Interactive and Iterative Discovery of Entity Network Subgraphs
Graph mining to extract interesting components has been studied in various guises, e.g., communities, dense subgraphs, cliques. However, most existing works are based on notions of frequency and connectivity and do not capture subjective interestingness from a user's viewpoint. Furthermore, existing approaches to mine graphs are not interactive and cannot incorporate user feedbacks in any natural manner. In this paper, we address these gaps by proposing a graph maximum entropy model to discover surprising connected subgraph patterns from entity graphs. This model is embedded in an interactive visualization framework to enable human-in-the-loop, model-guided data exploration. Using case studies on real datasets, we demonstrate how interactions between users and the maximum entropy model lead to faster and explainable conclusions
From Sets of Good Redescriptions to Good Sets of Redescriptions
International audienceRedescription mining aims at finding pairs of queries over data variables that describe roughly the same set of observations. These redescriptions can be used to obtain different views on the same set of entities. So far, redescription mining methods have aimed at listing all redescriptions supported by the data. Such an approach can result in many redundant redescriptions and hinder the user's ability to understand the overall characteristics of the data. In this work, we present an approach to find a good set of redescriptions, instead of finding a set of good redescriptions. That is, we present a way to remove the redundant redescriptions from a given set of redescriptions. We measure the redundancy using a framework inspired by the subjective interestingness based on maximum-entropy distributions as proposed by De Bie in 2011. Redescriptions, however, raise their unique requirements on the framework, and our solution differs significantly from the existing ones. Notably, our approach can handle disjunctions and conjunctions in the queries, whereas the existing approaches are limited only to conjunctive queries. The framework also reduces the redundancy in the redescription mining results, as we show in our empirical evaluation
Robust subgroup discovery
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.Comment: For associated code, see https://github.com/HMProenca/RuleList ;
submitted to Data Mining and Knowledge Discovery Journa
The Minimum Description Length Principle for Pattern Mining: A Survey
This is about the Minimum Description Length (MDL) principle applied to
pattern mining. The length of this description is kept to the minimum.
Mining patterns is a core task in data analysis and, beyond issues of
efficient enumeration, the selection of patterns constitutes a major challenge.
The MDL principle, a model selection method grounded in information theory, has
been applied to pattern mining with the aim to obtain compact high-quality sets
of patterns. After giving an outline of relevant concepts from information
theory and coding, as well as of work on the theory behind the MDL and similar
principles, we review MDL-based methods for mining various types of data and
patterns. Finally, we open a discussion on some issues regarding these methods,
and highlight currently active related data analysis problems
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