Group Pattern Mining on Moving Objects’ Uncertain Trajectories

Wang, Shuang; Wu, Liji; Zhou, Fuchai; Zheng, Cheng‐De; Wang, Haibo

doi:10.15837/ijccc.2015.3.1667

Cited by 11 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To mine such a group pattern, they developed AGP and VG-growth, derived from the Apriori algorithm and FP-growth algorithm. Wang et al [20] extended this to the uncertain group pattern -a group pattern that is discovered from the uncertain trajectory. Since the search space for the group pattern is extremely large, they designed an efficient pattern mining algorithm based on pruning.…”

Section: B Group Pattern Miningmentioning

confidence: 99%

Discovery of Loose Group Companion From Trajectory Data Streams

2020

View full text Add to dashboard Cite

The general usability of location tracking devices has been generated a high volume of spatial-temporal data in the form of trajectory. Exploring useful knowledge from these trajectory data can contribute to understanding many real-world applications, such as traffic monitoring and weather forecasting. The main task of trajectory data analysis is the tracking of an object group movement pattern. Existing algorithms, studying the evolving structure of moving object trajectories, have high computational complexity, particularly when tracking loose group companions. To address this problem, we describe a loose group companion tracking framework over trajectory data streams in an incremental manner, which reduces computational time. Loose group companion is the moving objects group that travels together. However, some members are allowed to leave at some timestamp. A crucial part of our framework is the micro-group based loose group companion discovery. It follows a moving object group and then incrementally detects the loose group companions. We validated our techniques using two real vehicle data sets and one synthetic data set. Our approach was, on average, 45% faster than previous algorithms.INDEX TERMS Group pattern, loose group companion, trajectory data stream, moving object clustering, spatial-temporal pattern.

show abstract

Section: B Group Pattern Miningmentioning

confidence: 99%

Discovery of Loose Group Companion From Trajectory Data Streams

2020

View full text Add to dashboard Cite

show abstract

“…The second is to define appropriate similarity functions and embed them to extensible clustering algorithms. Following this line, there are several approaches whose goal is to group whole trajectories, including: T-OPTICS (Nanni and Pedreschi 2006) that incorporates a distance function (Frentzos et al 2007) into the OPTICS algorithm (Ankerst 1999); the vector field k-means trajectory clustering technique (Ferreira et al 2013) whose central idea is to use vector fields to induce a notion of similarity between trajectories letting the vector fields themselves define and represent each cluster; CenTR-I-FCM (Pelekis et al 2011) a variant of Fuzzy C-means; and the concept of uncertain group pattern introduced in (Wang et al 2015). Both of the last two approaches propose specialized similarity functions having as goal to tackle the inherent uncertainty of trajectory data.…”

Section: Moving Clustersmentioning

confidence: 99%

On temporal-constrained sub-trajectory cluster analysis

Pelekis¹,

Tampakis²,

Vodas³

et al. 2017

Data Min Knowl Disc

View full text Add to dashboard Cite

Cluster analysis over Moving Object Databases (MODs) is a challenging research topic that has attracted the attention of the mobility data mining community. In this paper, we study the temporal-constrained sub-trajectory cluster analysis problem, where the aim is to discover clusters of sub-trajectories given an ad-hoc, user-specified temporal constraint within the dataset's lifetime. The problem is challenging because: (a) the time window is not known in advance, instead it is specified at query time, and (b) the MOD is continuously updated with new trajectories. Existing solutions first filter the trajectory database according to the temporal constraint, and then apply a clustering algorithm from scratch on the filtered data. However, this approach is extremely inefficient, when considering explorative data analysis where multiple clustering tasks need to be performed over different temporal subsets of the database, while the database is updated with new trajectories. To address this problem, we propose an incremental and scalable solution to the problem, which is built upon a novel indexing structure, called Representative Trajectory Tree (ReTraTree). ReTraTree acts as an effective spatiotemporal partitioning technique; partitions in ReTraTree correspond to groupings of sub-trajectories, which are incrementally maintained and assigned to representative (sub-)trajectories. Due to the proposed organization of sub-trajectories, the problem under study can be efficiently solved as simply as executing a query operator on ReTraTree, while insertion of new trajectories is supported. Our extensive experimental study performed on real and synthetic datasets shows that our approach outperforms a state-of-the-art in-DBMS solution supported by PostgreSQL by orders of magnitude.

show abstract

“…A novel algorithm was effectively designed in [20] to mine the uncertain group patterns that improve the mining efficiency but it not capable to handle more complex patterns.…”

Section: Related Workmentioning

confidence: 99%

Fusion Layer Topological Space Query Indexing For Uncertain Data Mining

Kalavathi¹,

Suresh²

2016

IJECS

View full text Add to dashboard Cite

-Data uncertainty is an intrinsic property in different applications such as sensor network monitoring, object recognition, location-based services (LBS), and moving object tracking. The data mining methods are applied to the above mentionedapplications their uncertainty has to be handled to achieve the accurate query results. The several probabilistic algorithm estimates the location and control for each object but not effective in handling query processing in distributed environments. Probabilistic inverted indexing computes the lower bound and upper bound for a threshold keyword but fails to extend the technique on tackle the correlation. In order to overcome the issues in uncertain data mining, Fusion Layer Topological Space Query Indexing technique (FLTS)is introduced. Initially, the queries are articulated on any random subset of attributes in the uncertain data.The FLTS index technique answers the top-k queries competently. FLTS index correctlyshows in their dominant relationships andsignificantly reduces the number of tuple accessed through query processing by pruning redundant tuple based on two criterions such as layer point sort method and the record point sort method. Initially, layer point sort method is used in FLTS indexto sort out tuple totally on the combination of all attribute values of the tuple. Subsequently, each attributes values particularly used for rating the tuple using record point sort method. Therefore,the correlation is removed significantly. Through an analysis of the interaction of the two sorting methods, derive a fixed bound that reduces the number of tuple retrieved during query processing and obtaining the correct query results in distributed environments. An experimental result shows that the FLTS indexing technique improves the query retrieving efficiency, response time, memory consumption and scalability.

show abstract

Group Pattern Mining on Moving Objects’ Uncertain Trajectories

Cited by 11 publications

References 17 publications

Discovery of Loose Group Companion From Trajectory Data Streams

Discovery of Loose Group Companion From Trajectory Data Streams

On temporal-constrained sub-trajectory cluster analysis

Fusion Layer Topological Space Query Indexing For Uncertain Data Mining

Contact Info

Product

Resources

About