Update README.md

README.md

@@ -23,6 +23,8 @@ It is implemented in the following steps:
 
 Spark iForest is designed and implemented easy to use. The usage is similar to the iForest sklearn implementation [3]. 
 
+In addition, pyspark package is also provided. More details and usage can be found in python folder.
+
 *Parameters:*
 
 - *numTrees:* The number of trees in the iforest model (>0).
@@ -48,7 +50,6 @@ If false, sampling without replacement is performed.
 - *predictionCol:* Prediction column name, default "prediction".
 
 
-
 ## Examples
 
 The following codes are an example for detecting anamaly data points using 
@@ -175,6 +176,20 @@ Thanks for his great contribution. In addition, thanks for the supports of Disco
 If you encounter any bugs, feel free to submit an issue or pull request. Also you can email to:
 <a href="fangzhou.yang@hotmail.com">Yang, Fangzhou (fangzhou.yang@hotmail.com)</a>
 
+## Citation
+
+Please cite spark-iforest in your publications if it helped your research. Here is an example BibTeX entry:
+
+```
+@misc{titicacasparkiforest,
+  title={spark-iforest},
+  author={Yang, Fangzhou and contributors},
+  year={2018},
+  publisher={GitHub},
+  howpublished={\url{https://github.com/titicaca/spark-iforest}},
+}
+```
+
 ## References:
 
 [1] Liu F T, Ting K M, Zhou Z, et al. Isolation Forest[C]. international conference on data mining, 2008.