Incrementally building frequent closed itemset lattice

• We propose the full maintenance process for frequent closed itemset lattice.
• With the index table, the search speed can be improved by finding the itemset in the sub-lattice.
• The theorems about the maximum children of a new node will help to reduce the mining time.
• Experimental results show that the proposed algorithm is more efficient than CLICL algorithm.

A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world applications, where new transactions are constantly inserted into databases. To build an efficient storage structure for mining association rules, this study proposes a method for building the initial frequent closed itemset lattice from the original database. The lattice is updated when new transactions are inserted. The number of database rescans over the entire database is reduced in the maintenance process. The proposed algorithm is compared with building a lattice in batch mode to demonstrate the effectiveness of the proposed algorithm.