All in One: Mining Multiple Movement Patterns

Abstract

International audienceRecent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. In common, these object sets are called object movement patterns. Due to the emergence of many different kinds of object movement patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of patterns. It is costly and time consuming to mine and manage various number of patterns, since we have to execute a large number of different pattern mining algorithms. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine movement patterns in the itemset context. Second, we propose a unifying approach, named GeT_Move, which uses a frequent closed itemset-based object movement pattern-mining algorithm to mine and manage different patterns. GeT_Move is developed in two versions which are GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the parameters setting, we further propose a Parameter Free Incremental GeT_Move algorithm. Comprehensive experiments are performed on real and large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches

Hospital healthcare flows: A longitudinal clustering approach of acute coronary syndrome in women over 45 years

“…Step a: we mined closed swarms using the Get_Move algorithm 16 with the following thresholds: min o = 1% of the group studied and min t = 2.…”

“…However, the Get_Move algorithm has the advantage of extracting many other patterns in a single pass. 16 In future work, we might enrich the knowledge on care trajectories that could lead to death 57 by investigating the convergent groups.…”

“…To meet the challenge in the predictive utility of health planning, we targeted the most frequent common care pathways. In a way, pattern-mining in care pathways is analogous to pattern-mining in moving objects 16 since a patient trajectory can be assimilated to a moving object trajectory. Indeed, as a moving object, a patient trajectory is a chronological succession of events occurring at different timestamps.…”

“…The latter principle is as follows (see Figure 1): from the NHDDB, we extracted patterns characterizing patient care trajectories by adapting a spatio-temporal pattern mining method. 16 The spatio-temporal patterns have been integrated into a visualization tool to trace the various ACS evolutions. Besides, we clustered these trajectories to identify temporal trends between stays and tariff trends as well.…”

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

“…Zhang et al [14] proposed the GMOVE pattern and adopted HMMs to build a model. And recently, Phan, N. et al [15] redefined the movement patterns and proposed a unifying approach, GeT_Move, which used a frequent closed itemset-based pattern mining algorithm to optimize the processing efficiency. And Lee, J.G.…”