"WOW – That's It"

<p>Please take some time to study and digest the information in this chart.</p><p><a href=”http://sceeto.com/components/com_wordpress/wp/wp-content/uploads/2012/03/03-29-12-Chart-examples-1.png”><img title=”S&amp;P 500 Exploratory Data Analysis | 30-MAR-12 | Courtesy of IOAMT” src=”http://sceeto.com/components/com_wordpress/wp/wp-content/uploads/2012/03/03-29-12-Chart-examples-1-300×242.png” alt=”S&amp;P 500 Exploratory Data Analysis | 30-MAR-12 | Courtesy of IOAMT” width=”300″ height=”242″ /></a></p>

A …sceeto user inquired about the Gaussian Filter settings that is used in some of the charts that we surface on this blog.

The charts that show the Gaussian Filter (recent examples here, here, and here) are generated by Billy Duryea of IOAMT.

Billy does an amazing job of integrating what may look like disparate pieces of information, but are really inter-connected pieces of the same market puzzle.

I reached out to Billy this evening to get his insight into his approach for Gaussian and he popped right back with the following valuable insight:

“Both the Gaussian and the super smother have a look-back window of 9 sample.

The combination of the gaussain and the super smother create the k-mean.

In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results into a partitioning of the data space into cells.

The problem is computationally difficult), however there are efficient algorithmsthat are commonly employed that converge fast to a local optimum.

These are usually similar to the expectation-maximization algorithm for mixturesof Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data, however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism finds clusters larger data sets

Suggest google’in K-mean mathworks statsistcs toolbox
http://www.mathworks.com/help/toolbox/stats/kmeans.html

Wolfram K-mean clustering algoritym
http://mathworld.wolfram.com/K-MeansClusteringAlgorithm.html“

Thanks Billy…your input and insights are always greatly appreciated.