ALL THINGS BIG AND SMALL 2.0

The AI machine was fed flag patterns like number of horizontal lines, vertical lines, colors, crescent, crosses, sun, stars, stripes, bars. It was then asked to cluster countries according to the degree of similarity of their flag patterns.  The result is in the map of six clusters. To test if the AI was good, I took the bottom right cluster (Pink) that contains Singapore. The countries placed near to Singapore are supposed to have flags with a high degree of similarity with Singapore's flag. Now look at the collage of flags image. The flags are: Top row from left: Singapore, Tunisia, Maldives Center from left: Turkey, Mauritania, Pakistan Bottom: Algeria, Comoros Islands, Indonesia. As you can see, although the colors of the flags vary, the flags do have similarities in terms of all of them having crescent and stars. So the AI was smart enough to recognize that the crescent and stars are the important variables.Although I gave all the patterns equal weighting in im...

The purpose of this experiment is to see whether an explorative data mining program could recognize degree of similarities in about 60 species of animals, from honeybees to lions, to starfish, parrots, etc. The software program used is from  www.viscovery.net  . Some uses of this program are: (a) clustering of customer data according to their behaviour. (b) In gene data analytics to identify biomarkers for the diagnosis of diseases. (c) For fraud profiling and forensic applications. The data is from the University of California Irvine data archive (archive.ics.uci.edu/ml). The ‘properties’ (attributes) considered were: backbone, legs, fins, lungs, hair, feathers, eggs, milk, tail, domestic, aquatic, airborne, venomous, predator.  Presence of an attribute is denoted by 1, and absence by 0, except for Legs, where it has a numerical value of: 2,4,6,8 legs. The tool was made to learn the attributes of all the animals and group them into clusters, each cluste...

1. Self-Organized clusters of S&P500 stocks, plus screened Growth, Quality and Value stocks 1. Statistics of the self-organized clusters shwoing the model variables of the screened stocks 3. Short/Long Areas of the self-organizing map. Though generated for serious analysis of the U.S. stock markets, these colorful self-organizing maps are attractive in an artistic way. Taken from my financial markets Blog, these images were generated using Self-Organizing Map technology [SOM], which is a form of Artificial Intelligence. In essence the map clustered elements according to their degree of similarity. Imagine it as a matrix with each row representing a stock, and each column filled with data on fundmental and technical characteristics of the stock e.g. its P/E, the last 5 years Return, the Sharpe Ratio, the estimated Earnings Per Share etc. All in there were about 30 variables for each stock. The SOM took into account all the variables of all the stocks simultaneously when plotting...