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Review of the Popular Association-Mining Algorithms - Apriori - Case Study Example

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The paper "Review of the Popular Association-Mining Algorithms - Apriori" highlights that algorithm that can mine classification over large databases with great efficiency and scalability is naive Bayes. This is due to its simplicity and efficiency in handling huge data sets…
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Review of the Popular Association-Mining Algorithms - Apriori
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Download file to see previous pages The four algorithm techniques which will be under analysis include AprioriTid, Apriori hybrid, Apriori, count distribution as well as Fast Distributed mining algorithm.
Association rule mining is a highly important technique used in data mining. It mainly extracts frequent patterns, interesting correlations and association in a given item set provided in a transaction database. It is a well-researched technique that aids in the discovery and derivation of interesting relationships between variables present in databases (Benoit 2002). Interestingness is used in different measures in the identification of strong rules in large databases. The main problem encountered in the development of association rules is identifying the one with confidence and support that is more than the available user-specified minimum confidence and minimum support (Arockiam et al. 2012).
Support (which is abbreviated as S) of an association rule refers to the percentage of records containing X∪Y over the total number of available records in a given database. For example, a 0.1% support of an item means only 1 out of 10 of the transactions in the database represents the purchase of that item (Arockiam et al. 2012).
Confidence on the other (abbreviated as C) of an association rule refers to the percentage of the number of transactions containing X∪Y over the total number of the transaction containing X. For example, a confidence of 80% signifies the presence of Y in 80% of X transactions (Arockiam et al. 2012).
Most algorithms perform well when data is in a centralized environment. In the case of a distributed database, mining useful pattern is more complicated. To make it more feasible, the data sets from the distributed databases have to be merged which would prove very costly. Therefore, for datasets distributed geographically, an algorithm that requires minimal communication costs is most favourable. ...Download file to see next pages Read More
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