Mining Spatial Gradual Patterns: Application to Measurement of Potentially Avoidable Hospitalizations

Ngo, Tu; Georgescu, V.; Laurent, Anne; Libourel, Thérèse; Mercier, Grégoire

doi:10.1007/978-3-319-73117-9_42

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…Extraction gradual itemsets from temporal data: Recently, in [5,28], the authors proposed an approach to extract gradual patterns in temporal data with an application on paleoecological databases to grasp functional groupings of coevolution of paleoecological indicators that model the evolution of the biodiversity over time. [29] introduces a generic method for extracting and analyzing gradual patterns in spatial data at several levels of granularity. The authors apply their method on the Health data to measure potentially avoidable hospitalization related with both societal and financial issues in public policies.…”

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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“…The research is being actively pursued for last one decade in gradual pattern mining such as mining the patterns from large numerical tabular datasets as well as addressing the scalability issues [4,5,8,9,10,11,12,13]. Also, the existing gradual pattern mining techniques presented in [4,5,10] are mainly for tabular databases while some other types are emerging, as for instance property graphs.…”

Section: Introductionmentioning

confidence: 99%

Handling missing values for mining gradual patterns from NoSQL graph databases

Shah

Castelltort

Laurent

2020

Future Generation Computer Systems

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Graph databases (NoSQL oriented graph databases) provide the ability to manage highly connected data and complex database queries along with the native graph-storage and processing. A property graph in a NoSQL graph engine is a labeled directed graph composed of nodes connected through edges with a set of attributes or properties in the form of (key : value) pairs. It facilitates to represent the data and knowledge that are in form of graphs. Practical applications of graph database systems have been seen in social networks, recommendation systems, fraud detection, and data journalism, as in the case for panama papers. Often, we face the issue of missing data in such kind of systems. In particular, these semi-structured NoSQL databases lead to a situation where some attributes (properties) are filled-in while other ones are not available, either because they exist but are missing (for instance the age of a person that is unknown) or because they are not applicable for a particular case (for instance the year of military service for a girl in countries where it is mandatory only for boys). Therefore, some keys can be provided for some nodes and not for other ones. In such a scenario, when we want to extract knowledge from these new generation database systems, we face the problem of missing data that arises need for analyzing them. Some approaches have been proposed to replace missing values so as to be able to apply data mining techniques. However, we argue that it is not relevant to consider such approaches because they may introduce biases or errors. In our work, we focus on the extraction of gradual patterns from property graphs that provide end-users with tools for mining correlations in the data when there exist missing values. Our approach requires first to define gradual patterns in the context of NoSQL property graph and then to extend existing algorithms so as to treat the missing values, because anti-monotonicity of the support can not be considered anymore in a simple manner. Thus, we introduce a novel approach for mining gradual patterns in the presence of missing values and we test it on real and synthetic data.

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“…Recently, in [17], the authors proposed an approach to extract gradual patterns in temporal data with an application on paleoecological databases to grasp functional groupings of coevolution of paleoecological indicators that model the evolution of the biodiversity over time. [18] introduce a generic method for extracting and analyzing gradual patterns in spatial data at several levels of granularity. The authors apply their method on the Health data to measure potentially avoidable hospitalization related with both societal and financial issues in public policies.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, recently [20] propose a Sequential Pattern Mining based approach for efficient extraction of frequent gradual patterns with their corresponding sequence of tuples. For instance, in [18], the authors show on geographical data that the analysis of the different sequence of objects associated to the gradual patterns allows to identify how an object participates in the associations between attribute variations. Therefore, it is often important for an algorithm to return a reasonable quantity of patterns with their associated extension.…”

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 Spatial Gradual Patterns: Application to Measurement of Potentially Avoidable Hospitalizations

Cited by 10 publications

References 8 publications

Discovering Frequent Gradual Itemsets with Imprecise Data

Discovering Frequent Gradual Itemsets with Imprecise Data

Handling missing values for mining gradual patterns from NoSQL graph databases

Mining Frequent Seasonal Gradual Patterns

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