Extensible Markov Model for Modelling Temporal Relationships Between Clusters
Building an EMM using New Data
Data stream clustering with tNN
Combining EMM Objects
DSC Interface for EMM and tNN (package stream)
Class "EMM"
Creator for Class "EMM"
Fading Cluster Structure and EMM Layer
Find the EMM State/Cluster for an Observation
Merge States of an EMM
Visualize EMM Objects
Predict a Future State
Prune States and/or Transitions
Reclustering EMM states
Remove States/Clusters or Transitions from an EMM
Score a New Sequence Given an EMM
Smooths transition counts between neighboring states/clusters
Create a Synthetic Data Stream
Class "tNN"
TRAC: Creating a Markov Model from a Regular Clustering
Class "TRACDS"
Access Transition Probabilities/Counts in an EMM
Extract a Transition Table for a New Sequence Given an EMM
Update a TRACDS temporal structure with new state assignements
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. Hahsler and Dunham (2010) <doi:10.18637/jss.v035.i05>.