This project uses the Latent Dirichlet Allocation (LDA) algorithm to cluster a fuzzy time-series. More details can be found on this report.
LDA is a generative statistical model [Wikipedia] proposed by David Blei, Andrew Ng and Michael I. Jordan. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics [Original Paper]. The best example of its usage is the clustering of a text corpora into a set of topics based on their words, i.e., each document is described as a distribution of topics and each topic as a distribution of words. A very intuitive explanation about LDA can be found here. A very important feature of LDA is that it is able to discard topics automatically if needed, i.e., the algorithm may assign probabilities greated than zero for a smaller number of topics you originally set.
The input time-series is fuzzified with a given fuzzy form and universe of discourse. This means that each value in the time series will become a word in this universe. A sliding window with a fixed size through these words create a set of documents. Then, each document will be assigned to a set of topics with different probabilities.
Please, use the run.m or run.r files to see the examples.