2 research outputs found
ConiVAT: Cluster Tendency Assessment and Clustering with Partial Background Knowledge
The VAT method is a visual technique for determining the potential cluster
structure and the possible number of clusters in numerical data. Its improved
version, iVAT, uses a path-based distance transform to improve the
effectiveness of VAT for "tough" cases. Both VAT and iVAT have also been used
in conjunction with a single-linkage(SL) hierarchical clustering algorithm.
However, they are sensitive to noise and bridge points between clusters in the
dataset, and consequently, the corresponding VAT/iVAT images are often
in-conclusive for such cases. In this paper, we propose a constraint-based
version of iVAT, which we call ConiVAT, that makes use of background knowledge
in the form of constraints, to improve VAT/iVAT for challenging and complex
datasets. ConiVAT uses the input constraints to learn the underlying similarity
metric and builds a minimum transitive dissimilarity matrix, before applying
VAT to it. We demonstrate ConiVAT approach to visual assessment and single
linkage clustering on nine datasets to show that, it improves the quality of
iVAT images for complex datasets, and it also overcomes the limitation of SL
clustering with VAT/iVAT due to "noisy" bridges between clusters. Extensive
experiment results on nine datasets suggest that ConiVAT outperforms the other
three semi-supervised clustering algorithms in terms of improved clustering
accuracy.Comment: Submitted to IEEE Transactions on Knowledge and Data Engineerin
A Scalable Framework for Trajectory Prediction
Trajectory prediction (TP) is of great importance for a wide range of
location-based applications in intelligent transport systems such as
location-based advertising, route planning, traffic management, and early
warning systems. In the last few years, the widespread use of GPS navigation
systems and wireless communication technology enabled vehicles has resulted in
huge volumes of trajectory data. The task of utilizing this data employing
spatio-temporal techniques for trajectory prediction in an efficient and
accurate manner is an ongoing research problem. Existing TP approaches are
limited to short-term predictions. Moreover, they cannot handle a large volume
of trajectory data for long-term prediction. To address these limitations, we
propose a scalable clustering and Markov chain based hybrid framework, called
Traj-clusiVAT-based TP, for both short-term and long-term trajectory
prediction, which can handle a large number of overlapping trajectories in a
dense road network. Traj-clusiVAT can also determine the number of clusters,
which represent different movement behaviours in input trajectory data. In our
experiments, we compare our proposed approach with a mixed Markov model
(MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for
both short- and long-term trajectory predictions. We performed our experiments
on two real, vehicle trajectory datasets, including a large-scale trajectory
dataset consisting of 3.28 million trajectories obtained from 15,061 taxis in
Singapore over a period of one month. Experimental results on two real
trajectory datasets show that our proposed approach outperforms the existing
approaches in terms of both short- and long-term prediction performances, based
on prediction accuracy and distance error (in km).Comment: Accepted in IEEE Transactions on Intelligent Transportation System.
Info: 15 Pages, 9 Figures, 5 Table