http://code.activestate.com/recipes/tags/data_mining/2014-06-16T13:20:01-07:00ActiveState Code Recipes2014-06-16T13:20:01-07:00Roberto Bellohttp://code.activestate.com/recipes/users/4189498/http://code.activestate.com/recipes/578893-kindex-for-som-neural-networks-python/

<p style="color: grey"> Python recipe 578893 by <a href="/recipes/users/4189498/">Roberto Bello</a> (<a href="/recipes/tags/cv/">cv</a>, <a href="/recipes/tags/data_mining/">data_mining</a>, <a href="/recipes/tags/means/">means</a>, <a href="/recipes/tags/neural_network/">neural_network</a>, <a href="/recipes/tags/python/">python</a>, <a href="/recipes/tags/som/">som</a>). </p> <p>Achieved cataloging into groups by a SOM neural network, the question arises whether or not there is knowledge in the groups, namely whether the groups are between them distinct and have homogeneous characteristics within each group. The use of the coefficient of variation (CV) can be of help.</p> <p>KINDEX (Knowledge Index) is an index that measures how much knowledge is contained in the groups obtained from the SOM neural network: in the case KINDEX reaches the maximum value of 1, each group would consist of records with constant values ​​in all the variables / columns, and each group would be quite distinct from other groups. KINDEX is calculated using the weighted-average CV of variables / columns groups, comparing them to the CV of the variables / columns of the input file before cataloging.</p>

2014-03-19T11:46:16-07:00Roberto Bellohttp://code.activestate.com/recipes/users/4189498/http://code.activestate.com/recipes/578853-kb-neural-data-mining-python-sources/

<p style="color: grey"> Python recipe 578853 by <a href="/recipes/users/4189498/">Roberto Bello</a> (<a href="/recipes/tags/data_mining/">data_mining</a>, <a href="/recipes/tags/neural_network/">neural_network</a>, <a href="/recipes/tags/python/">python</a>). </p> <p><a href="http://www.freeopen.org/?p=85" rel="nofollow">http://www.freeopen.org/?p=85</a> The aim of this book is to present and describe in detail the algorithms to extract the knowledge hidden inside data using Python language, which allows us to read and easily understand the nature and the characteristics of the rules of the computing utilized, as opposed to what happens in commercial applications, which are available only in the form of running codes, which remain impossible to modify.</p> <p>The algorithms of computing contained within the book are minutely described, documented and available in the Python source format, and serve to extract the hidden knowledge within the data whether they are textual or numerical kinds. There are also various examples of usage, underlining the characteristics, method of execution and providing comments on the obtained results.</p>

2011-06-04T11:02:18-07:00Gabriel Synnaevehttp://code.activestate.com/recipes/users/4178198/http://code.activestate.com/recipes/577735-expectation-maximization/

<p style="color: grey"> Python recipe 577735 by <a href="/recipes/users/4178198/">Gabriel Synnaeve</a> (<a href="/recipes/tags/data_mining/">data_mining</a>, <a href="/recipes/tags/machine_learning/">machine_learning</a>). Revision 3. </p> <p>Quick and simple implementation of Gaussian mixture model (with same covariance shapes) based expectation-maximization algorithm.</p>

2011-06-30T13:39:29-07:00jrovegnohttp://code.activestate.com/recipes/users/4170207/http://code.activestate.com/recipes/577773-extract-air-quality-data-of-santigo-chile-on-csv-f/

<p style="color: grey"> Python recipe 577773 by <a href="/recipes/users/4170207/">jrovegno</a> (<a href="/recipes/tags/aire/">aire</a>, <a href="/recipes/tags/calidad/">calidad</a>, <a href="/recipes/tags/chile/">chile</a>, <a href="/recipes/tags/data_mining/">data_mining</a>, <a href="/recipes/tags/santiago/">santiago</a>). </p> <p>Extract air quality data of Santigo , Chile on csv file</p>

2011-06-04T10:58:09-07:00Gabriel Synnaevehttp://code.activestate.com/recipes/users/4178198/http://code.activestate.com/recipes/577734-k-means/

<p style="color: grey"> Python recipe 577734 by <a href="/recipes/users/4178198/">Gabriel Synnaeve</a> (<a href="/recipes/tags/data_mining/">data_mining</a>, <a href="/recipes/tags/machine_learning/">machine_learning</a>). Revision 2. </p> <p>Hard and soft k-means implemented simply in python (with numpy). Quick and dirty, tested and works on large (10k+ observations, 2-10 features) real-world data.</p>