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The gradual patterns that model the complex co-variations of attributes ofthe form "The more/less X, The more/less Y" play a crucial role in many realworld applications where the amount of numerical data to manage is important,this is the biological data. Recently, these types of patterns have caught theattention of the data mining community, where several methods have been definedto automatically extract and manage these patterns from different data models.However, these methods are often faced the problem of managing the quantity ofmined patterns, and in many practical applications, the calculation of allthese patterns can prove to be intractable for the user-defined frequencythreshold and the lack of focus leads to generating huge collections ofpatterns. Moreover another problem with the traditional approaches is that theconcept of gradualness is defined just as an increase or a decrease. Indeed, agradualness is considered as soon as the values of the attribute on bothobjects are different. As a result, numerous quantities of patterns extractedby traditional algorithms can be presented to the user although theirgradualness is only a noise effect in the data. To address this issue, thispaper suggests to introduce the gradualness thresholds from which to consideran increase or a decrease. In contrast to literature approaches, the proposedapproach takes into account the distribution of attribute values, as well asthe user's preferences on the gradualness threshold and makes it possible toextract gradual patterns on certain databases where literature approaches faildue to too large search space. Moreover, results from an experimentalevaluation on real databases show that the proposed algorithm is scalable,efficient, and can eliminate numerous patterns that do not verify specificgradualness requirements to show a small set of patterns to the user.

Discovering Frequent Gradual Itemsets with Imprecise Data