Discovering Frequent Gradual Itemsets with Imprecise Data

TL;DR

This paper introduces a novel method for discovering frequent gradual itemsets in imprecise data by incorporating gradualness thresholds and attribute value distributions, improving pattern relevance and computational efficiency.

Contribution

It proposes a new approach that considers gradualness thresholds and data distribution, addressing limitations of traditional methods and reducing pattern noise.

Findings

The proposed algorithm is scalable and efficient on real databases.

It effectively eliminates irrelevant patterns based on gradualness thresholds.

The method outperforms traditional approaches in managing pattern quantity.

Abstract

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. 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 and makes it possible to extract gradual patterns on certain databases where literature approaches 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.