Implements TRACDS (Temporal Relationships between Clusters for Data Streams), a generalization of Extensible Markov Model (EMM). TRACDS adds a temporal or order model to data stream clustering by superimposing a dynamically adapting Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN data stream clustering). Development of this package was supported in part by NSF IIS-0948893 and R21HG005912 from the National Human Genome Research Institute.
|Author||Michael Hahsler [aut, cre, cph], Margaret H. Dunham [aut, cph]|
|Date of publication||2015-07-24 07:54:05|
|Maintainer||Michael Hahsler <email@example.com>|
16S: Count Data for 16S rRNA Sequences
build: Building an EMM using New Data
cluster: Data stream clustering with tNN
combine: Combining EMM Objects
conversion: TRAC: Creating an EMM from a Regular Clustering
Derwent: Derwent Catchment Data
EMM: Creator for Class "EMM"
EMM-class: Class "EMM"
EMMsim: Synthetic Data to Demonstrate EMMs
EMMTraffic: Hypothetical Traffic Data Set for EMM
fade: Fading Cluster Structure and EMM Layer
find_clusters: Find the EMM State/Cluster for an Observation
merge: Merge States of an EMM
plot.EMM: Visualize EMM Objects
predict: Predict a Future State
prune: Prune States and/or Transitions
recluster: Reclustering EMM states
remove: Remove States/Clusters or Transitions from an EMM
score: Score a New Sequence Given an EMM
smooth_transitions: Smooths transition counts between neighboring states/clusters
synthetic_stream: Create a Synthetic Data Stream
tNN-class: Class "tNN"
TRACDS-class: Class "TRACDS"
transition: Access Transition Probabilities/Counts in an EMM
transition_table: Extract a Transition Table for a New Sequence Given an EMM
update: Update a TRACDS temporal structure with new state...