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rEMM (version 0.1-2)

Extensible Markov Model (EMM) for Data Stream Clustering in R

Description

The Extensible Markov Model (EMM) adds a temporal component to data stream clustering by superimposing a dynamically adapting Markov Chain. This package implements EMM on top of threshold NN, a simple clustering algorithm for data streams.

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Version

Install

install.packages('rEMM')

Monthly Downloads

762

Version

0.1-2

License

GPL-2

Maintainer

Michael Hahsler

Last Published

January 26th, 2010

Functions in rEMM (0.1-2)

plot

Visualize EMM Objects
predict

Predict a Future State
remove

Remove States/Clusters or Transitions from an EMM
build

Building an EMM using New Data
find_states

Find the EMM State/Cluster for an Observation
Derwent

Derwent Catchment Data
prune

Prune States and/or Transitions
transition_table

Extract a Transition Table for a New Sequence Given an EMM
16S

Count Data for 16S rRNA Sequences
synthetic_stream

Create a Synthetic Data Stream
fade

Fading Cluster Structure and EMM Layer
transition

Access Transition Probabilities/Counts in an EMM
merge_states

Merge States of an EMM
tNN-class

Class "tNN"
EMM-class

Class "EMM"
score

Score a New Sequence Given an EMM
EMMsim

Synthetic Data to Demonstrate EMMs
EMMLayer-class

Class "EMMLayer"
recluster

Reclustering EMM states
EMMTraffic

Hypothetical Traffic Data Set for EMM