Renáta Iváncsy

A time- and memory-efficient frequent itemset discovering algorithm for association rule mining

Frequent itemset discovering is a highly researched area in the field of data mining. The algorithms dealing with this problem have several advantages and disadvantages regarding their time complexity, I/O cost and memory requirement. There are algorithms that have moderate memory usage but high I/O cost, thus the execution time of them is high; such methods are for example the level-wise algorithms. Other methods have advantageous time behaviour; however, they are memory intensive, like the two-phase algorithms. In this paper, a novel algorithm, which is efficient both in time and memory, is proposed. The new algorithm discovers the small frequent itemsets quickly by taking advantage of the easy indexing opportunity of the suggested candidate storage structure. The main benefit of the novel algorithm is its advantageous time behaviour when using different types of datasets as well as its low I/O activity and moderate memory requirement.

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%