rEMM (version 1.1.1)

TRACDS-class: 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.

% \item{\code{initial_counts}:}{Object of class \code{"numeric"} indicating % how often a state is the first state in a sequence % (used to calculate initial state probabilities for the Markov chain).}

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.

% \item{initial_transition}{\code{signature(x = "TRACDS")}: ...} % \item{predict}{\code{signature(object = "TRACDS")}: ... }

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.

% \item{transition_matrix}{\code{signature(x = "TRACDS")}: ... } % \item{transition}{\code{signature(x = "TRACDS", from = "data.frame", to = "missing")}: ... } % \item{transition}{\code{signature(x = "TRACDS", from = "character", to = "character")}: ... }

transitions

signature(x = "TRACDS"): returns all transitions as a matrix of state names with a from and a to column.

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. 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 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.

See Also

Look at transition, transition_matrix and initial_transition to access the transition information in the EMM. predict is used to predict future states of an EMM. '>EMM extends "TRACDS".