How can you use this to cluster textual data

[drreddy]

Hello,

I'am interested to know is there any provision to cluster text data, I see that you are taking coordinates as input. Can you explain how to extend it for clustering a set of text documents.

[mgaido91]

The usual way to cluster text documents is made as follows:
1 - the data is transformed using the TF-IDF (https://en.wikipedia.org/wiki/Tf%E2%80%93idf) normalization
2 - the proper clustering technique (as DBSCAN can be) is applied to the transformed data, by using the cosine distance measure

As far as the TF-IDF transformation is regarded, you've to implement it by your own or to look to existing implementations. Instead, for the DBSCAN you can use this fork by me (https://github.com/speedymrk9/spark_dbscan) which extends @alitouka's repo allowing the usage of the cosine distance measure, which is the one you need.

Hope I've been clear, I'm at your disposal for any doubt.

[drreddy]

Hey thanks for the quick reply, but if the cosine distance is found the memory complexity would be O(n^2) right. I meant is there any way one can convert the text data into the coordinates your package requires. So that I can use your efficient implementation.

[mgaido91]

Sorry but I have not understood what you've said. May you explain me? The way to convert text documents into coordinates is the TFIDF transformation.

----- Messaggio originale -----
Da: "D. Rajeev. Reddy" notifications@github.com
Inviato: ‎01/‎09/‎2015 17:13
A: "alitouka/spark_dbscan" spark_dbscan@noreply.github.com
Cc: "Marco Gaido" marcogaido91@gmail.com
Oggetto: Re: [spark_dbscan] How can you use this to cluster textual data (#7)

but if the cosine distance is found the memory complexity would be O(n^2) right. I meant is there any way one can convert the text data into the coordinates your package requires. So that I can use your efficient implementation.
—
Reply to this email directly or view it on GitHub.