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Evaluation of Apriori Algorithm on Retail Market Transactional Database to get Frequent Itemsets

Gaikwad Pooja R., Shailesh D. Kamble Shailesh D., Thakur Nileshsingh V., Patharkar Akshay S.

Annals of Computer Science and Information Systems

2017

Vol. 10

187--192

In Data mining the concept of association rule mining (ARM) is used to identify the frequent itemsets from large datasets. It defines frequent pattern mining from larger datasets using Apriori algorithm \& FP-growth algorithm. The Apriori algorithm is a classic traditional algorithm for the mining all frequent itemsets and association rules. But, the traditional Apriori algorithm have some limitations i.e. there are more candidate sets generation \& huge memory consumption, etc. Still, there is a scope for improvement to modify the existing Apriori algorithm for identifying frequent itemsets with a focus on reducing the computational time and memory space. This paper presents the analysis of existing Apriori algorithms and results of the traditional Apriori algorithm. Experimentation carried out on transactional database i.e. retail market for getting frequent itemsets. The traditional Apriori algorithm is evaluated in terms of support and confidence of transactional itemsets.

Comparative evaluation of the different data mining techniques used for the medical database

Kasperczuk A., Dardzińska A.

Acta Mechanica et Automatica

2016

Vol. 10, no. 3

233--238

Data mining is the upcoming research area to solve various problems. Classification and finding association are two main steps in the field of data mining. In this paper, we use three classification algorithms: J48 (an open source Java implementation of C4.5 algorithm), Multilayer Perceptron - MLP (a modification of the standard linear perceptron) and Naïve Bayes (based on Bayes rule and a set of conditional independence assumptions) of the Weka interface. These classifiers have been used to choose the best algorithm based on the conditions of the voice disorders database. To find association rules over transactional medical database first we use apriori algorithm for frequent item set mining. These two initial steps of analysis will help to create the medical knowledgebase. The ultimate goal is to build a model, which can improve the way to read and interpret the existing data in medical database and future data as well.