Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions.
|Date of publication||2017-01-08 11:20:09|
|Maintainer||Alexis Sarda <firstname.lastname@example.org>|
clusterSim: Cluster Similarity Matrix
compute_envelop: Time series warping envelops
create_dtwclust: Create formal 'dtwclust' objects
cvi: Cluster validity indices
DBA: DTW Barycenter Averaging
dtw2: DTW distance with L2 norm
dtw_basic: Basic DTW distance
dtwclust: Time series clustering
dtwclust-class: Class definition for 'dtwclust'
dtwclustControl-class: Class definition for 'dtwclustControl'
dtwclustFamily-class: Class definition for 'dtwclustFamily'
dtwclust-methods: Methods for 'dtwclust'
dtwclust-package: Time series clustering along with optimizations for the...
dtw_lb: DTW distance matrix guided by Lemire's improved lower bound
GAK: Fast global alignment kernels
lb_improved: Lemire's improved DTW lower bound
lb_keogh: Keogh's DTW lower bound
NCCc: Cross-correlation with coefficient normalization
randIndex: Compare partitions
reinterpolate: Wrapper for simple linear reinterpolation
SBD: Shape-based distance
shape_extraction: Shape average of several time series
TADPole: TADPole clustering
uciCT: Subset of character trajectories data set
zscore: Wrapper for z-normalization