Mining Multi-Relational Gradual Patterns

Phan, Nhat Hai; Ienco, Dino; Malerba, Donato; Poncelet, Pascal; Teisseire, Maguelonne

doi:10.1137/1.9781611974010.95

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Extraction gradual itemsets from stream data: In [23], an approach based on B-Trees and OWA (Ordered Weighted Aggregation) operator [24,25] is proposed to mine data streams for gradual patterns. [26] proposes the relational gradual pattern concept, which enables to examine the correlations between attributes from a graduality point of view in multi-relational data.…”

Section: On the Gradual Itemsets Extraction From The Complex Datamentioning

confidence: 99%

Discovering Frequent Gradual Itemsets with Imprecise Data

Boujike,

Lonlac,

Tsopze

et al. 2020

Preprint

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The gradual patterns that model the complex co-variations of attributes of the form "The more/less X, The more/less Y " play a crucial role in many real world applications where the amount of numerical data to manage is important, this is the biological data, or medical data. Recently, these types of patterns have caught the attention of the data mining community, where several methods have been defined to automatically extract and manage these patterns from different data models. However, these methods are often faced the problem of managing the quantity of mined patterns, and in many practical applications, the calculation of all these patterns can prove to be intractable for the user-defined frequency threshold and the lack of focus leads to generating huge collections of patterns. Moreover another problem with the traditional approaches is that the concept of gradualness is defined just as an increase or a decrease. Indeed, a gradualness is considered as soon as the values of the attribute on both objects are different. As a result, numerous quantities of patterns extracted by traditional algorithms can be presented to the user although their gradualness is only a noise effect in the data. To address this issue, this paper suggests to introduce the gradualness thresholds from which to consider an increase or a decrease. In contrast to literature approaches, the proposed approach takes into account the distribution of attribute values, as well as the user's preferences on the gradualness threshold. The proposed algorithm makes it possible to extract gradual patterns on certain databases where state-of-the art gradual patterns mining algorithms fail due to too large search space. Moreover, results from an experimental evaluation on real databases show that the proposed algorithm is scalable, efficient, and can eliminate numerous patterns that do not verify specific gradualness requirements to show a small set of patterns to the user.

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Section: On the Gradual Itemsets Extraction From The Complex Datamentioning

confidence: 99%

Discovering Frequent Gradual Itemsets with Imprecise Data

Boujike,

Lonlac,

Tsopze

et al. 2020

Preprint

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“…for expressing another kind of knowledge. For example, on the multi-relational database, the notion of gradual patterns was extended to the case in which the co-variations are possibly expressed between attributes of different database relations [15].…”

Section: Related Workmentioning

confidence: 99%

“…[7]; in financial markets, where one would like to discover co-evolution between financial indicators, or in marketing for analyzing client databases [8]. Several works have addressed the mining of gradual patterns and different algorithms have been designed for discovering gradual patterns from different numerical data models (e.g., temporal, stream, relational, or noisy data) [8,9,10,11,12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Extracting Seasonal Gradual Patterns from Temporal Sequence Data Using Periodic Patterns Mining

Lonlac,

Doniec,

Lujak

et al. 2020

Preprint

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“…For instance, in [12], an approach based on B-Trees and OWA (Ordered Weighted Aggregation) operator [13], [14] is proposed to mine data streams for gradual patterns. [15] propose the relational gradual pattern concept, which enables to examine the correlations between attributes from a graduality point of view in multi-relational data.…”

Section: Introductionmentioning

confidence: 99%

Mining Frequent Seasonal Gradual Patterns

Lonlac

Doniec

Lujak

et al. 2020

Big Data Analytics and Knowledge Discovery

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Mining frequent episodes aims at recovering sequential patterns from temporal data sequences, which can then be used to predict the occurrence of related events in advance. On the other hand, gradual patterns that capture co-variation of complex attributes in the form "When X increases/decreases, Y increases/decreases" play an important role in many real world applications where huge volumes of complex numerical data must be handled. More recently, they have received attention from the data mining community for exploring temporal data and methods have been defined to automatically extract gradual patterns from temporal data. However, to the best of our knowledge, no method has been proposed to extract gradual patterns that always appear at the identical time intervals in the sequences of temporal data, despite the knowledge that such patterns may bring in certain applications. In this paper, we propose to extract co-variations of periodically repeating attributes from the sequences of temporal data that we call seasonal gradual patterns. We discuss the specific features of these patterns and propose an approach for their extraction by exploiting motif mining algorithms in a sequence, and justify its applicability to the gradual case. Illustrative results obtained from a real world data set are described and show the interest for such patterns.

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Mining Multi-Relational Gradual Patterns

Cited by 7 publications

References 19 publications

Discovering Frequent Gradual Itemsets with Imprecise Data

Discovering Frequent Gradual Itemsets with Imprecise Data

Extracting Seasonal Gradual Patterns from Temporal Sequence Data Using Periodic Patterns Mining

Mining Frequent Seasonal Gradual Patterns

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