Performance prediction for association rule mining algorithms

Execution time prediction is very important issue in job scheduling and resource allocation. Association rule mining algorithms are complex and their execution time depends on both the properties of the input data sources and on the mining parameters. In this paper, an analytical model of the Apriori algorithm is introduced, which is based on statistical parameters of the input dataset (average size of the transactions, number of transactions in the dataset) and on the minimum support threshold. The developed analytical model has only few parameters therefore the predicted execution time can be calculated in a simple way. The investigated domain of the input parameters covers the most commonly used datasets, therefore the introduced model can be used widely in field of association rule mining. The constant parameters of the model can be identified in small number of test executions. The developed model allows predicting the execution time of the Apriori algorithm in a wide range of parameters. The suggested model was validated by several different datasets and the experimental results show that the overall average error rate of the model is less than 15%