Machine Learning and Data Mining – Association Analysis with Python by Marcel Caraciolo.
From the post:
Recently I’ve been working with recommender systems and association analysis. This last one, specially, is one of the most used machine learning algorithms to extract from large datasets hidden relationships.
The famous example related to the study of association analysis is the history of the baby diapers and beers. This history reports that a certain grocery store in the Midwest of the United States increased their beers sells by putting them near where the stippers were placed. In fact, what happened is that the association rules pointed out that men bought diapers and beers on Thursdays. So the store could have profited by placing those products together, which would increase the sales.
Association analysis is the task of finding interesting relationships in large data sets. There hidden relationships are then expressed as a collection of association rules and frequent item sets. Frequent item sets are simply a collection of items that frequently occur together. And association rules suggest a strong relationship that exists between two items.
When I think of associations in a topic map, I assume I am at least starting with the roles and the players of those roles.
As this post demonstrates, that may be overly optimistic on my part.
What if I discover an association but not its type or the roles in it? And yet I still want to preserve the discovery for later use?
An incomplete association as it were.