A Study on Incremental Association Rule Mining

Abstract- Applying data mining techniques to real-world applications is a challenging task because the databases are dynamic i.e., changes are continuously taking place due to addition, deletion, modification etc., of the contained data. Generally if the dataset is incremental in nature, the frequent item sets discovering problem consumes more time. Once in a while, the new records are added in an incremental dataset. Generally when compared to the entire data set, the size of the increments or the number of records added to the dataset is very small. But the assumption of the rules in the updated dataset may get distorted due to the addition of these new records. Hence a few new association rules may be created and a few old ones may become obsolete. When new transactions are inserted into the original databases, traditional batch-mining algorithms resolve this problem by reprocessing the entire new databases. But they require much computational time and ignore the available mined knowledge. This paper elicits the importance of incremental mining and discusses about various incremental mining algorithms that exist in the literature