Tree-based incremental association rule mining without candidate itemset generation

As time advances new transactions are added to the databases. The extensive amounts of knowledge and data stored in databases require the development of specialized tools for storing and accessing of data, data analysis and effective use of stored knowledge of data. An incremental association rule discovery can create an intelligent environment such that new information or knowledge such as changing customer preferences or new seasonal trends can be discovered in a dynamic environment. The goal is to present how methods and tools for intelligent data analysis are helpful in narrowing the increasing gap between data gathering and data comprehension. There is a greatest challenge in candidate generation for large data with low support threshold. In this paper, we proposed Tree-based Incremental Association Rule Mining (TIARM) algorithm to deal with this problem. The proposed algorithm uses novel data structure INC-Tree, it is an extension of FP-Tree to improve storage compression and allow frequent pattern mining without generation of candidate itemsets. Our algorithm allows mining with a single pass over the database as well as efficient insertion or deletion of transactions at any time. Experimental results reveal that our proposed algorithm has better performance than other algorithms.