We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may consist of a combination of attribute values that are nominal sequences, time series, sets, and traditional relational values. Datasets of this type occur naturally in many domains including health care, financial analysis, complex system diagnostics, and domains in which multi-sensors are used. AprioriSetsAndSequences identifies predefined events of interest in the sequential data attributes. It then mines for association rules that make explicit all frequent temporal relationships among the occurrences of those events and relationships of those events and other data attributes. Our algorithm inherently handles different levels of time granularity in the same data set. We have implemented AprioriSetsAndSequences within the Weka environment and have applied it to computer performance, stock market, and clinical sleep disorder data. We show that AprioriSetsAndSequences produces rules that express significant temporal relationships that describe patterns of behavior observed in the data set.

Data mining is an important process of extracting desirable knowledge from existing databases for specific purposes. Nearly all transactions in real-world databases involve items bought, quantities of the items, and the time periods in which they appear. In the past, temporal quantitative mining was proposed to find temporal quantitative rules from a temporal quantitative database. However, the quantitative values of items are not suitable to human reasoning. To deal with this, the fuzzy set theory was applied to the temporal quantitative mining because of its simplicity and similarity to human reasoning. In this thesis, we thus handle the problem of mining fuzzy temporal association rules from a publication database, and propose three algorithms to achieve it. The three algorithms handle different lifespan definitions, respectively. In the first algorithm, the lifespan of an item is evaluated from the time of the first transaction with the item to the end time of the whole database. In the second algorithm, an additional publication table, which includes the publication date of each item in stores, is given, and thus the lifespan of an item is measured by its entire publication period. Finally in the third algorithm, the lifespan of an item is calculated from the end time of the whole database to its earliest time in the database for the item to be a fuzzy temporal frequent item within the duration. In addition, an effective itemset table structure is designed to store and get information about itemsets and can thus speed up the execution efficiency of the mining process. At last, experimental results on two simulation datasets compare the mined fuzzy temporal quantitative itemsets and rules with and without consideration of lifespans of items under different parameter settings.

Data mining techniques have been widely applied to extract desirable knowledge from existing databases for specific purposes. In real-world applications, a database usually involves the time periods when transactions occurred and exhibition periods of items, in addition to the items bought in the transactions. To handle this kind of data, temporal data mining techniques are thus proposed to find temporal association rules from a database with time. Most of the existing studies only consider different item lifespans to find general temporal association rules, and this may neglect some useful information. For example, while an item within the whole exhibition period may not be a frequent one, it may be frequent within part of this time. To deal with this, the concept of a hierarchy of time is thus applied to temporal data mining along with suitable time granules, as defined by users. In this thesis, we thus handle the problem of mining temporal association rules with a hierarchy of time granules from a temporal database, and also propose three novel mining algorithms for different item lifespan definitions. In the first definition, the lifespan of an item in a time granule is calculated from the first appearance time to the end time in the time granule. In the second definition, the lifespan of an item in a time granule is evaluated from the publication time of the item to the end time in the time granule. Finally, in the third definition, the lifespan of an item in a time granule is measured by its entire exhibition period. The experimental results on a simulation dataset show the performance of the three proposed algorithms under different item lifespan definitions, and compare the mined temporal association rules with and without consideration of the hierarchy of time granules under different parameter settings.

There are two main contributions in the thesis . Firstly, we design a novel and efficient algorithm for mining calendar-based association rules which have multilevel time granularities in temporal databases. Unlike apriori-like approaches , our method scans the database twice at most. By avoiding multiple scans over the database , our method can reduce the database scanning time. Secondly, we use membership functions to construct fuzzy calendar patterns which represent asynchronous periods. With the use of fuzzy calendar patterns, we can discover fuzzy periodical association rules which are association rules occurring in asynchronous periods. Experimental results have shown that our method is more efficient than others, and we can find fuzzy periodical association rules satisfactorily.

This thesis focuses on mining association rules on multivariate time series. Com-mon association rule mining algorithms can usually only be applied to transactional data, and a typical application is market basket analysis. If we want to mine temporal association rules on time series data, changes need to be made. During temporal association rule mining, the temporal ordering nature of data and the temporal interval between the left and right patterns of a rule need to be considered. This thesis reviews some mining methods for temporal association rule mining, and proposes two similar algorithms for the mining of frequent patterns in single and multivariate time series. Both algorithms are scalable and efficient. In addition, temporal association rules are generated from the patterns found. Finally, the usability and efficiency of the algorithms are demonstrated by evaluating the results.

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Le pneumocoque est une cause majeure d'infections bactériennes communautaires dans le monde. D'après la littérature, la consommation d'antibiotiques pourrait influer sur le risque de colonisation ou d'infection par pneumocoque à sensibilité diminuée aux antibiotiques spécifiques. La France, grande consommatrice d'antibiotiques, a mis en place, à l'automne 2002, un plan national pour préserver l'efficacité des antibiotiques et améliorer leur usage. Cette campagne a conduit à une diminution significative de la consommation d'antibiotiques durant les périodes hivernales. En 2003, une vaccination anti-pneumococcique des enfants de moins de deux ans a été recommandée afin de réduire les infections communautaires à pneumocoque chez l'enfant. Au vu du contexte français, il paraît important d'étudier la dynamique des infections invasives communautaires à pneumocoque en prenant en compte les deux interventions de santé publique. L'étude de l'association entre deux ou plusieurs séries temporelles saisonnières doit être effectuée sur des séries stationnarisées afin d'éliminer tout risque de confusion. Les différentes méthodes de désaisonnalisation ont été comparées à travers une étude de simulations afin d'identifier la stratégie optimale. De plus, le modèle de régression linéaire adapté aux séries temporelles repose sur l'hypothèse de la linéarité du lien. Cependant, cette hypothèse est critiquable en particulier lorsqu'on s'intéresse au lien associé à une série de type épidémique. Une deuxième étude de simulations a été réalisée afin d'étudier l'impact de l'hypothèse de la linéarité du lien lors de son estimation.A partir des stratégies permettant d'étudier le lien entre plusieurs séries saisonnières identifiées à partir des études de simulations, la dynamique des infections invasives communautaires à pneumocoque a été étudiée en France entre janvier 2002 et décembre 2009. / Streptococcus pneumoniae is a leading cause of communitary-acquired pneumococcal invasive infections worldwide. Recent surveys studied the association between pneumococcal carriage and antibiotic consumption. Reducing antibiotic consumption migth reduce pneumococcal carriage. In France, a national campaign was launched in 2002 in order to reduce antibiotic consumption mainly in the community. In 2003, the seven-valent pneumococcal conjugate vaccine was introduced and recommanded for to children in order to reduce the risk of invasive pneumococcal infections. In this contexte, it is worth investigating the evolution of communitary-acquired pneumococcal invasive infections in France.When examining the association between two monthly time series data with some common seasonal pattern, we are faced with the problem of eliminating this seasonal variation. Indeed this common seasonal feature will act as a confounder if not removed. Even if several methods exist, such as the use of semi-parametric or trigonometric functions, no optimal method has been yet identified. Hence, we compared performances of available smoothing approaches to estimate a temporal link between two series using extensive simulations. The linear regression usually used to estimate the link between two time series is based on the hypothesis of a linear link. However, such a link might not be linear when considering an association with an epidemic time series. In order to check whether this linear model can also manage non linear relationships, a simulation study was also settled. Finally, from these simulation studies, we identified strategies that where implemented to estimate the association between community-acquired pneumococcal invasive infections and antibiotic exposure.