Artificial neural network for incremental data mining

Abstract

In this paper, we present a Health Check process (HC) based on artificial neural network (ANN). Our approach aim is to detect Incremental Apriori deviation (IncA) proposed in previous work used in order to minimize processing time and explore new incoming data only. HC process use germinated infrequent items and generated frequent itemset to run correction according to predicted value. Experiments on datasets show that deviations are detected and IncA generate same results as classic Apriori while saving processing time. Also, experiment results show that HC learning ameliorate with time.