One of the objectives of spatio-temporal data mining is to analyze moving object datasets to exploit interesting patterns. Traditionally, existing methods only focus on an unchanged group of moving objects during a time period. Thus, they cannot capture object moving trends which can be very useful for better understanding the natural moving behavior in various real world applications. In this paper, we present a novel concept of "time relaxed gradual trajectory pattern", denoted real-Gpattern, which captures the object movement tendency. Additionally, we also propose an efficient algorithm, called ClusterGrowth, designed to extract the complete set of all interesting maximal real-Gpatterns. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.

Abstract. Recent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, these object sets are called spatio-temporal patterns. Analyzing such data has been applied in many real world applications, e.g., in ecological study, vehicle control, mobile communication management, etc. However, few tools are available for flexible and scalable analysis of massive scale moving objects. Additionally, there is no framework devoted to efficiently manage multiple kinds of patterns at the same time. Motivated by this issue, we propose a framework, named GeT Move, which is designed to extract and manage different kinds of spatio-temporal patterns concurrently. A user-friendly interface is provided to facilitate interactive exploration of mining results. Since GeT Move is tested on many kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data by exhibiting different kinds of patterns efficiently.

Abstract. Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we investigate two different problems. The first one is related to the extraction of representative terms from a set of tweets. More precisely we address the following question: are traditional information retrieval measures appropriate when dealing with tweets?. The second problem is related to the evolution of tweets over time for a set of users. With the development of data mining approaches, lots of very efficient methods have been defined to extract patterns hidden in the huge amount of data available. More recently new spatio-temporal data mining approaches have specifically been defined for dealing with the huge amount of moving object data that can be obtained from the improvement in positioning technology. Due to particularity of tweets, the second question we investigate is the following: are spatio-temporal mining algorithms appropriate for better understanding the behavior of communities over time? These two problems are illustrated through real applications concerning both health and political tweets.

Recent improvements in positioning technology have led to a much wider availability of massive moving object data. One of the objectives of spatiotemporal data mining is to analyze such datasets to exploit moving objects that travel together. Naturally, the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Thus, there are time gaps among moving object clusters. Existing approaches either put a strong constraint (i.e. no time gap) or completely relaxed (i.e. whatever the time gaps) in dealing with the gaps may result in the loss of interesting patterns or the extraction of huge amount of extraneous patterns. Thus it is difficult for analysts to understand the object movement behavior. Motivated by this issue, we propose the concept of fuzzy swarm which softens the time gap constraint. The goal of our paper is to find all non-redundant fuzzy swarms, namely fuzzy closed swarm. As a contribution, we propose fCS-Miner algorithm which enables us to efficiently extract all the fuzzy closed swarms. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.

Les développements récents des techniques de géolocalisation ont généré de larges volumes de données associées aux objets mobiles. Une des tâches d'analyse de telles données reste à identifier les objets évoluant ensemble. Cette problématique peut être résolue par les motifs spatiotemporels et de nombreuses propositions ont été réalisées ces dernières années. Néanmoins chacune de ces approches se focalise sur un type de motif spécifique. Il est alors coûteux de vouloir tous les obtenir car il est nécessaire d'exécuter l'ensemble des algorithmes proposés. Pour répondre à ce problème, nous redéfinissons les motifs spatiotemporels dans le contexte des itemsets et proposons une approche unifiée, appelée GeT_Move, permettant d'extraire de tels motifs. Cet algorithme est proposé en deux versions dont l'une est incrémentale. Les expérimentations réalisées sur des données réelles et des données synthétiques soulignent l'efficacité de notre proposition qui surpasse les approches existantes. ABSTRACT. Recent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, they are called spatio-temporal patterns. Due to the emergence of many different kinds of spatio-temporal patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of pattern. In addition to the fact that it is a painstaking task due to the large number of algorithms used to mine and manage patterns, it is also time consuming. To address these issues, we first redefine spatiotemporal patterns in the itemset context. Secondly, we propose a unifying approach, named GeT_Move, using a frequent closed itemset-based spatio-temporal pattern-mining algorithm to mine and manage different spatio-temporal patterns. GeT_Move is implemented in two versions which are GeT_Move and Incremental GeT_Move. Experiments are performed on real and synthetic datasets and the experimental results show that our approaches are very effective and outperform existing algorithms in terms of efficiency. MOTS-CLÉS : motif spatiotemporel, itemset clos fréquent, trajectoires.