Classification in real time without prior knowledge of the number of classes - real-time

Question

Is there an implemented algorithm (with python/R or java in preference) that can classify incoming data from an unknown generator with absolutely no prior knowledge or assumption.
For example:
Let G be a generator of 2d vectors that generate one vector in each second.
What we know, and nothing else, is that this vectors are separable into clusters in space (euclidean distance).
Question: How can I classify my data in real time so that at each iteration, the algorithm propose clusters?

Answer 1

I'm also in the process of searching for something related to Data stream clustering and I found some papers and code:
Aforementioned survey by Charu C. Aggarwal from aforementioned book;
Density-Based Clustering over an Evolving Data Stream with Noise. by Feng Cao et al., proposes DenStream; here is a git repo for that (Matlab);
Density-Based Clustering for Real-Time Stream Data by Yixin Chen, Li Tu, proposes the D-Stream framework (2008 version called Stream data clustering based on grid density and attraction); There is a DD-Stream that I can't find a pdf for in A grid and density-based clustering algorithm for processing data stream by Jia.
A Fast and Stable Incremental Clustering Algorithm by Steven Young et al. focuses on clustering as an unsupervised learning process;
Self-Adaptive Anytime Stream Clustering by Philipp Kranen et al. has ClusTree and this git repo implements DClusTree;
Pre-clustering algorithm for anomaly detection and clustering that uses variable size buckets by Manish Sharma et al. is more recent and may be relevant (git repo of the author);
This paper is about MOA (Massive Online Analysis) states that it implements some of the above (StreamKM++, CluStream, ClusTree, Den-Stream, D-Stream and CobWeb). I believe that D-Stream is work in progress/wishful thinking (is not part of the pre-release available from their website). MOA is written in Java, here is streamMOA package.
The code in this repository seems to be a Python implementation of the D-Stream but, according to the author, it is slow.
Also, stream is a framework for data stream clustering research with R.

Answer 2

I think you are asking about "Stream mining" here.
Read this article
Chapter 10: A Survey of Stream Clustering Algorithms.
Charu C. Aggarwal, IBM T. J. Watson Research Center, Yorktown Heights, NY
This can be found in the 2014 book
DATA CLUSTERING- Algorithms and Applications, Edited by Charu C. Aggarwal and Chandan K. Reddy.
In that chapter the "CluStream" framework is described. This project is from 2002, and it is based on the BIRCH algorithm from 1997 which is a "Micro-Clustering" approach. The algorithm creates an index structure on the fly.
Considering that there are few BIRCH implementations,
there is probably no open-source CluStream algorithm/framework available.
Here's a Github repo with a BIRCH implementation in Java - although I haven't tried this code, and that repo is not for "stream mining".
All this just appeared on my radar because I just recently participated in the Coursera MOOC on cluster analysis.

Answer 3

There are no assumption free methods.
You are asking for magic to happen.
Never blindly use a clustering result. Do not use clustering on a stream. Instead, analyze and correct any clustering result before deployment.
Watch out for hidden assumptions. For example, assumptions that clusters are convex, distance based (why is Euclidean distance the coorect choice?), have the same size or extend, are separated (by what?) or shape. Whenever you design a method, you make assumptions on what is interesting.
Wothout assumption, anything is a "clustering"!