Linguistic and Graphical Explanation of a Cluster-Based Data Structure

Smits, Grégory; Pivert, Olivier

doi:10.1007/978-3-319-23540-0_13

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Some approaches perform a post-processing clustering step on the set Σ α Q to determine its structure as groups of somewhat similar answers [8]. The diversified result set is then composed of the most representative tuples taken from each of these clusters.…”

Section: Diversified Searchmentioning

confidence: 99%

Diversifying Top-k Answers in a Query by Example Setting

Smits,

Lesot,

Pivert

et al. 2023

Lecture Notes in Computer Science

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For a given data base T and a user query Q, the top-k answers are the k tuples from T that best match Q. The integration of a diversity constraint aims at avoiding returning redundant tuples, that are too similar one to another. This paper addresses the diversification question in the Query By Example setting, especially for approaches that can deal with possibly very different representative examples provided by the user. It proposes a new definition for diversity that depends on the query, in order to guarantee that the result set illustrates the diversity of the representative examples provided by the user, covering all components of the query. The paper proposes a numerical measure to assess diversity in that sense, an algorithm to identify such a diversified top-k set, optimising both the query satisfaction and the diversity measure, as well as its integration into a flexible querying approach.

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Section: Diversified Searchmentioning

confidence: 99%

Diversifying Top-k Answers in a Query by Example Setting

Smits,

Lesot,

Pivert

et al. 2023

Lecture Notes in Computer Science

Self Cite

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“…To describe the data inner structure [14], the underlying vocabulary has to fit data distribution. In [8], a measure has been proposed to quantify the adequacy between the data inner structure and the one induced by a fuzzy vocabulary.…”

Section: A Fuzzy Vocabulary Inferencementioning

confidence: 99%

“…A key issue for human-in-the-loop data analysis approaches is to provide users with a complete and interpretable description of the data inner structure using terms from their own vocabulary [14]. To reach this goal, two important subsequent questions have to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Vocabulary-Based Detection and Explanation of Anomalies

Nath,

Smits,

Pivert

2023

2023 IEEE International Conference on Fuzzy Systems (FUZZ)

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Fuzzy partitions associated with linguistic variables are particularly useful to provide users with a description of the data. However, designing a fuzzy vocabulary that makes it possible to linguistically describe the data distribution and its inner structure is a tedious task. This paper introduces a novel strategy to infer possible fuzzy partitions from the data distribution with the objective to have available modalities to describe both dense and sparse regions. A data inner structure as well as the anomalies are then identified using this vocabulary whose terms are also used to provide users with contrastive explanations about the found anomalies.

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“…As mentioned in Section II, one of the challenges is the huge amount of produced results. We propose to address this issue using a graphical representation, that is in line with the user demands: domain experts faced with data analysis tasks are nowadays mainly looking for graphical views of the data and of the extracted knowledge [19], [20].…”

Section: Visualization Of the Extracted Linguistic Summariesmentioning

confidence: 99%

Efficient Generation of Reliable Estimated Linguistic Summaries

Smits

Nerzic

Pivert

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Summarizing data with linguistic statements is a crucial and topical issue that has been largely addressed by the soft computing community. The goal of summarization is to generate statements that linguistically describe the properties observed in a dataset. This paper addresses the issue of efficiently extracting these summaries and rendering them to the final user, in the case where the data to be summarized are stored in a relational data base: it proposes a novel strategy that leverages the statistics about the data distribution maintained by the database system. This paper shows that reliable summaries can be very efficiently estimated based on these statistics only and without any costly data access. Additionally, it proposes a visualization of the set of extracted summaries that offers a fruitful interactive exploration tool to the user. Experiments performed on two real data bases show the relevance and efficiency of the proposed approach: with a negligible loss of accuracy, we provide the first linguistic summarization approach whose processing time does not depend on the size of the dataset. The generation of estimated linguistic summaries takes less than one second even for dataset containing millions of tuples.

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Linguistic and Graphical Explanation of a Cluster-Based Data Structure

Cited by 8 publications

References 18 publications

Diversifying Top-k Answers in a Query by Example Setting

Diversifying Top-k Answers in a Query by Example Setting

Fuzzy-Vocabulary-Based Detection and Explanation of Anomalies

Efficient Generation of Reliable Estimated Linguistic Summaries

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