Finding relational redescriptions

Galbrun, Esther; Kimmig, Angelika

doi:10.1007/s10994-013-5402-3

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…Redescription mining was introduced by Ramakrishnan et al (2004) for Boolean data. Later, Zaki and Ramakrishnan (2005) and Parida and Ramakrishnan (2005) further developed the theory of Boolean redescription mining, Galbrun and Miettinen (2012a) extended it to real-valued data, and Galbrun and Kimmig (2014) studied redescription mining in relational data.…”

Section: Related Workmentioning

confidence: 99%

From sets of good redescriptions to good sets of redescriptions

2018

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Redescription 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 identify and remove the redundant redescriptions, that is, an approach to move from a set of good redescriptions to a good set of redescriptions. We measure the redundancy of a redescription using a framework inspired by the concept of subjective interestingness based on maximum-entropy distributions as proposed by De Bie in 2011. Redescriptions, however, generate specific 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. Our framework can also handle data with Boolean, nominal, or real-valued data, possibly containing missing values, making it applicable to a wide variety of data sets. Our experiments show that our framework can efficiently reduce the redundancy even on large data sets.

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Section: Related Workmentioning

confidence: 99%

From sets of good redescriptions to good sets of redescriptions

2018

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“…Galbrun & Miettinen (2012b) extend the greedy approach by Gallo et al (2008) to work on numerical data. Redescription mining was extended by Galbrun & Kimmig (2013) to a relational and by Galbrun & Miettinen (2012a) to an interactive setting. Recently, two tree-based algorithms have been proposed by Zinchenko (2014), which explore the use of decision trees in a non-Boolean setting and present different methods of layer-by-layer tree construction, which make informed splits at each level of the tree.…”

Section: Related Work In Redescription Miningmentioning

confidence: 99%

A framework for redescription set construction

Mihelčić

Džeroski

Lavrač

et al. 2017

Expert Systems with Applications

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Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented as rules inferred from one or more disjoint sets of attributes, called views. As such, they support knowledge discovery process and help domain experts in formulating new hypotheses or constructing new knowledge bases and decision support systems. In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences. Construction of large and heterogeneous redescription set relies on CLUS-RM algorithm and a novel, conjunctive refinement procedure that facilitates generation of larger and more accurate redescription sets. The work also introduces the variability of redescription accuracy when missing values are present in the data, which significantly extends applicability of the method. Crucial part of the framework is the redescription set extraction based on heuristic multi-objective optimization procedure that allows user to define importance levels towards one or more redescription quality criteria. We provide both theoretical and empirical comparison of the novel framework against current state of the art redescription mining algorithms and show that it represents more efficient and versatile approach for mining redescriptions from data.

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“…Since the introduction of redescription mining by Ramakrishnan et al in 2004 [31], a few problem variants [8], [28], [36], several algorithms [9], [13], [31], [37], as well as an interactive mining tool [11], [12] have been introduced. Redescription mining is a descriptive approach, tailored towards exploratory data analysis.…”

Section: A Redescription Miningmentioning

confidence: 99%

From Sets of Good Redescriptions to Good Sets of Redescriptions

Kalofolias

Galbrun

Miettinen

2016

2016 IEEE 16th International Conference on Data Mining (ICDM)

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Redescription 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.

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Finding relational redescriptions

Cited by 9 publications

References 22 publications

From sets of good redescriptions to good sets of redescriptions

From sets of good redescriptions to good sets of redescriptions

A framework for redescription set construction

From Sets of Good Redescriptions to Good Sets of Redescriptions

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