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An Efficient Technique for Mining Top Ranked Data from a Standard Data Set

Hiresh Udiya, Rupesh Kabra, Dharmendra Magal

Abstract


The amount of data being collected is increasing rapidly. The main reason is the use of computerized applications.
Because of that reason the valuable information is hidden in large amount of data. It is attracting researchers of
multiple disciplines to study effective approaches to derive useful knowledge from vast data. Among various data
mining objectives, the mining of association rules has been the focus of knowledge discovery in databases.
Today mining of top ranked data from a large data set is one of the most fundamental task in data mining. However,
depending on the choice of the parameters (the minimum confidence and minimum support), current algorithms can
become very slow and generate an extremely large amount of results or generate too few results, omitting valuable
information. This is a serious problem because in practice users have limited resources for analyzing the results and
thus are often only interested in discovering a certain amount of results, and fine tuning the parameters is timeconsuming.
To address this problem, we propose a unique technique to mine top ranked data from a data set. The
algorithm utilizes a new approach for generating association rules. The algorithm has excellent performance and
scalability, and that it is an advantageous alternative to classical association rule mining algorithms when the user
wants to control the number of rules generated.

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