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What is association rule in WEKA?

What is association rule in WEKA?

Association rule learners find associations between attributes. Between any attributes: there’s no particular class attribute. Rules can predict any attribute, or indeed any combination of attributes. The most popular association rule learner, and the one used in Weka, is called Apriori.

What is association rule mining with example?

So, in a given transaction with multiple items, Association Rule Mining primarily tries to find the rules that govern how or why such products/items are often bought together. For example, peanut butter and jelly are frequently purchased together because a lot of people like to make PB&J sandwiches.

How is association rule mining performed in Weka?

The users can also build their machine learning methods and perform experiments on sample datasets provided in the WEKA directory. Data visualization in WEKA can be performed using sample datasets or user-made datasets in .arff,.csv format. Association Rule Mining is performed using the Apriori algorithm.

How does apriori work in association rule mining?

Association Rule Mining with WEKA. Apriori in WEKA starts with the upper bound support and incrementally decreases support (by delta increments which by default is set to 0.05 or 5%). The algorithm halts when either the specified number of rules are generated, or the lower bound for min. support is reached.

What is the problem of association rule mining?

Data mining has been given much attention in database communities due to its wide applicability. The problem of mining association rules from transactional database was introduced in [1]. The concept aims to find frequent patterns, interesting correlations, associations among sets of items in the transaction databases or other data repositories.

Which is the minimum value for lift in Weka?

WEKA allows the resulting rules to be sorted according to different metrics such as confidence, In this example, we have selected lift as the criteria. entered 1.5 as the minimum value for lift (or improvement) is computed as the confidence of the rule divided by the support of the right-hand-side (RHS). In a simplified form, given a rule L =>