Paper Info
Title
A Novel Approach to Trajectory Analysis Using String Matching and Clustering
Abstract
Clustering of sub-trajectories is a very useful method to extract important information from vast amounts of trajectory data. Existing trajectory clustering algorithms have focused on geometric properties and spatial features of trajectories and sub-trajectories. In contrast to the existing trajectory clustering algorithms, we propose a new framework to cluster sub-trajectories based on a combination of their spatial and non-spatial features. This algorithm combines techniques from grid based approaches, spatial geometry and string processing. First, we convert each trajectory into a representative sequence that captures the trajectory direction and location. We identify common sub-trajectories from all the sequences using a modified string matching algorithm. Then, we extract non-spatial features from the common sub-trajectories. Finally, we present a density based clustering algorithm to cluster the sub-trajectories. Experimental results show that our framework correctly discovers groups of similar sub-trajectories with their similar non-spatial features.
Year
DOI
Venue
2013
10.1109/ICDMW.2013.130
ICDM Workshops
Keywords
Field
DocType
common sub-trajectories,trajectory clustering algorithm,trajectory analysis,novel approach,similar non-spatial feature,trajectory direction,string matching,existing trajectory,clustering algorithm,trajectory data,similar sub-trajectories,non-spatial feature,spatial feature
Data mining,Fuzzy clustering,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,k-medians clustering,Canopy clustering algorithm,Clustering high-dimensional data,Data stream clustering,Pattern recognition,Correlation clustering,Machine learning
Conference
ISSN
Citations 
PageRank 
2375-9232
5
0.48
References 
Authors
10
3
Name
Order
Citations
PageRank
Madhuri Debnath1112.74
Praveen Kumar Tripathi217911.83
Ramez Elmasri31950756.86