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Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori

The classification of frequent item set is a vital problem in data mining. In recent area, where it deals with more outcome within limited time, there is a need of certain algorithm supported by faster computation. The aim of this research work is to generate a frequent item set so as to achieve a comparatively reduced time and effort. Frequent item set is a set of items that is often bought with another item, for example bread and butter. Here, two association rules mining algorithm, that is, Apriori and Eclat are used to predict the probability of subordinate items being bought if a prime item is obtained. Apriori algorithm works by picking the frequent individual items in a database and extending them to larger and larger sets whereas Eclat algorithm uses depth first search for discovering frequent item sets. In this paper, a dataset of transactions in a retail company is taken and corresponding support, confidence and lift is estimated. Here the relationship between these variables is explored. It is observed in Eclat algorithm that even after varying the minimum support the maximum and minimum support remains constant and using the Apriori algorithm that increasing the minimum lift value results in a gradual decrease in maximum support and small increase in minimum confidence values.