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Select the Consequent for Associations
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Associations are powerful means in finding strong correlations among your dataset values. However, depending on your case, you may not always be interested in finding the strongest relationships but only the rules that meet certain conditions instead. Specifying your conditions to zero in on your rules of interest is now easier than ever. Simply select your data field of interest and one or more field values for the consequent part of the rule, and you will obtain the relevant associations in no time.

associations consequent antecedent fields dashboard api association
Associations datasets
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BigML associations let you discover meaningful relationships among dataset fields and their values. Now you can easily select the rules that you want from the list of associations found by BigML and create a filtered dataset from those. This allows you to conduct further analysis on the filtered dataset instances matching those association rules.

datasets filter items winter2016 association dashboard
Association Discovery
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BigML team has worked really hard to launch Association Discovery this Fall 2015. We are proud to be the first cloud-based platform that offers this unsupervised Machine Learning method to find meaningful relationships between values in high-dimensional datasets. BigML acquired Magnum Opus from professor Geoff Web (Monash University, Melbourne) combining the best-in-class Association Discovery technology with BigML easy-to-use platform.

web unsupervised machine learning algorithm fall2015 association market basket analysis
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