Added readme

README.md

+# textClust
+
+This R package implements an algorithm to cluster text streams as proposed in our paper:
+
+> Carnein M., Assenmacher D. and Trautmann H. (2017), "Stream Clustering of Chat Messages with Applications to Twitch Streams", In Advances in Conceptual Modeling: ER 2017 Workshops AHA, MoBiD, MREBA, OntoCom, and QMMQ, Valencia, Spain, November 6--9, 2017, Proceedings. , pp. 79-88. Springer International Publishing.
+
+
+The algorithm uses a widely popuar two-phase clustering approach where the stream is first summarised in real time. The result are many small preliminary clusters in the stream called 'micro-clusters'. Our micro-clusters maintain enough information to update them over time and also efficiently calculate the cosine similarity between them based on the Tf-idf vector of their texts. Upon request, the miro-clusters can be reclustered to generate the final result using any distance-based clustering algorithm such as hierarchical clustering. To keep the micro-clusters up-to-date our algorithm applies a fading strategy where micro-clusters that are not updated regularly loose relevance and are eventually removed.
+
+
+## Installation
+
+The easiest way to install the package is by using devtools:
+
+```R
+devtools::install_git("https://wiwi-gitlab.uni-muenster.de/stream/textClust")
+```
+
+Alternatively, the package can be build from source.
+
+
+## Usage
+
+Usage and interfaces are largely based on the R-package [stream](https://github.com/mhahsler/stream) with modifications for the analysis of text data
+
+```R
+## read text-stream from file
+stream = DSD_ReadCSV("file.txt", sep = "\t", comment.char="", quote="")
+
+## define text clustering algorithm
+algorithm = DSC_textClust(r=.5, lambda=0.5, tgap=100, updateAll = F, nmin=1, nmax=1, k=10, verbose=F)
+
+## run the algorithm
+update(algorithm, stream, n=1000)
+
+## plot the resulting micro-clusters using multi-dimensional scaling (MDS)
+plot(algorithm, numRepresentatives = 2, type = "micro")
+
+## or plot the final clusters
+plot(algorithm, numRepresentatives = 2, type = "macro")
+```
+
+The algorithm can also be evaluated using prequential (also interleaved test-then-train) evaluation:
+
+```R
+evaluation = textClust::evaluate_cluster(algorithm, stream, measure=c("numMicroClusters", "purity"), n=500000, assign="micro", type="micro", assignMethod="nn", horizon=100)
+```
+
+## Data
+
+The algorithm assumes that the data-file comes in the following form (without the header):
+
+| Time | User | Text |
+| -----| -----| ---- |
+| 03/28/2017 14:59:48 | Albert Einstein |	The difference between stupidity and genius is that genius has its limits. |
+| 03/28/2017 15:00:10 | Dennis |	Well said, Albert! |
+| 03/28/2017 15:00:43 | Albert Einstein |	Thank you! |
+
+In our paper, we used the implemented algorithm to cluster chat messages from the streaming platform Twitch. In general, however, any kind of text data can be analysed.