Knowledge Discovery with Unsupervised Learning

Fascinating example using Bayesia's graphical methods.   I like how the visualization leads to useful  implications of the discovery.  Easy to follow example with stock market data.  Links to a complete tutorial.   Why Stocks?  They motivate:

" .... This area can perhaps serve as a very practical proof of the powerful properties of Bayesian networks, as we can quickly compare machine-learned findings with our own understanding of market dynamics. For instance, the prevailing opinions among investors regarding the relationships between major stocks should be reflected in any structure that is to be discovered by our algorithms.

More specifically, we will utilize the unsupervised and supervised learning algorithms of the BayesiaLab software package to automatically generate Bayesian networks from daily stock returns over a six-year period. We will examine 459 stocks from the S&P 500 index, for which observations are available over the entire timeframe. We selected the S&P 500 as the basis for our study, as the companies listed on this index are presumably among the best-known corporations worldwide, so even a casual observer should be able to critically review the machine-learned findings. In other words, we are trying to machine-learn the obvious, as any mistakes in this process would automatically become self-evident. Quite often experts’ reaction to such machine-learned findings is, “well, we already knew that.” That is the very point we want to make, as machine-learning can — within seconds — catch up with human expertise accumulated over years, and then rapidly expand beyond what is already known.expertise accumulated over years, and then rapidly expand beyond what is already known. .... "