R: Class "TRACDS"

TRACDS-class {rEMM}

R Documentation

Class "TRACDS"

Description

Representation of the temporal structure of a data stream clustering using a extensible Markov model.

Objects from the Class

Objects can be created using the creator function TRACDS or by directly calling new("TRACDS", ...). Most slots for the extended classes can be used as parameters.

Slots

lambda:

Object of class "numeric" specifying the rate for fading.

lambda_factor:

Object of class "numeric" expressing the fading rate expressed as a factor.

tracds_d:

An environment containing all the variable data of the TRACDS object:

mm:: Object of class "SimpleMC" representing the first order Markov model of the EMM.
current_state:: Object of class "character" with the name of current state in the EMM. NA means no current state.

Methods

copy: signature(x = "TRACDS"): Make a copy of the TRACDS object. Making explicit copies is necessary since information is stored in an environment which is not copied for regular assignements.
current_state: signature(x = "TRACDS"): returns the name of the current state.
nstates: signature(x = "TRACDS"): returns the number of states.
ntransitions: signature(x = "TRACDS"): returns the number of transitions with a count larger than 0 stored in the object.
plot: signature(x = "TRACDS", y = "missing"): Plots the object as a directed graph.
states: signature(x = "TRACDS"): returns the names of the states.
transitions: signature(x = "TRACDS"): returns all transitions as a matrix of state names with a from and a to column.

Note

A TRACDS object can be coerced to igraph or graph objects using as.igraph() and as.graph().

References

Michael Hahsler and Margaret H. Dunham. Temporal structure learning for clustering massive data streams in real-time. In SIAM Conference on Data Mining (SDM11), pages 664–675. SIAM, April 2011. doi:10.1137/1.9781611972818.57

M. Hahsler, M. H. Dunham (2010): rEMM: Extensible Markov Model for Data Stream Clustering in R, Journal of Statistical Software, 35(5), 1-31, URL doi:10.18637/jss.v035.i05

M.H. Dunham, Y. Meng, J. Huang (2004): Extensible Markov Model, In: ICDM '04: Proceedings of the Fourth IEEE International Conference on Data Mining, pp. 371–374.