Advances in Computers, Vol. 13

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Finding frequent itemsets is a type of counting activity. But unlike producing a simple tally of items we observe in a dataset (today we sold 80 carrots and 100 tomatoes), finding frequent itemsets is slightly different. Specifically, to find frequent itemsets we look for co-occurring sets of items within some larger group. These larger groups are sometimes imagined as supermarket transactions or shopping baskets, and the entire exercise is sometimes called market basket analysis. Staying with the supermarket analogy, the items co-occurring within those baskets are sometimes imagined to be combinations of products purchased at the supermarket.

A project – discovering association rules in software project tags In 1997, the website, Freshmeat, was created as a directory that tracked free, libre, and open source software (FLOSS) projects. In 2011, the site was renamed Freecode. After sales and acquisitions and several site redesigns, in 2014 all updates to the Freecode site were discontinued. The site remains online, but it is no longer being updated and no new projects are being added to the directory. Freecode now serves as a snapshot of facts about FLOSS projects during the late 1990s and 2000s.

After that, we are simply calculating based on previously found counts. An important principle that will help us find frequent itemsets faster is called the upward closure property. Upward closure states that an itemset can only be frequent if all the items in it are also frequent. In other words, there is no sense in calculating the support for any itemset if all the itemsets contained in it are not also frequent. [ 28 ] Chapter 2 Why is it important to know about closure? Because knowing this rule will save us a lot of time in calculating the possible itemsets.

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