ST-DCONTOUR: a serial, density-contour based spatio-temporal clustering approach to cluster location streams.
Yongli ZhangChristoph F. EickPublished in: IWGS@SIGSPATIAL (2016)
Keyphrases
- spatio temporal
- clustering algorithm
- density based clustering algorithm
- data clustering
- hierarchical clustering
- arbitrary shape
- cluster analysis
- intra cluster
- k means
- data points
- overlapping clusters
- inter cluster
- cluster membership
- cluster centers
- data streams
- unsupervised clustering
- hierarchical clustering algorithm
- validity measures
- clustering procedure
- clustering approaches
- clustering method
- dense regions
- supervised clustering
- stream clustering
- clustering framework
- clustering scheme
- model based clustering
- clustering result
- clustering analysis
- agglomerative hierarchical clustering
- subspace clustering
- cluster centroids
- spatial and temporal relationships
- constrained clustering
- disjoint clusters
- rough k means
- overlapping clustering
- document clustering
- moving objects
- data objects
- movement patterns
- fuzzy clustering
- unsupervised learning
- homogeneous groups
- image sequences
- density distribution
- instance level constraints
- multiple data streams
- cluster validity
- meaningful clusters
- semi supervised clustering
- categorical data
- density based clustering
- clustering quality
- cluster structure
- similar objects
- cluster validation
- outlier detection
- sliding window
- space time
- evolutionary clustering
- spatial and temporal
- shape descriptors
- clustering ensemble
- data sets