[3Rin4-79] A Case Study of Stream and Density-Based Clustering for Floating Car Data
Keywords:Time Series Data Analysis, Clustering, Location Data, ITS
Extracting stay points from location data is important for the detection of point-of-interests or facilities. In this
paper, we develop a method to extract stay points via density-based clustering algorithms, by using positional
data gathered from floating cars (FCD). Through our analysis, we found a characteristic of FCD when extracting
stay-points: the extracted clusters are highly affected by the space-time range of input data. We discuss this
characteristic and future directions by showing the clustering results from real data.
paper, we develop a method to extract stay points via density-based clustering algorithms, by using positional
data gathered from floating cars (FCD). Through our analysis, we found a characteristic of FCD when extracting
stay-points: the extracted clusters are highly affected by the space-time range of input data. We discuss this
characteristic and future directions by showing the clustering results from real data.
Authentication for paper PDF access
A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.