A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

Improving Efficiency of Incremental Mining by Trie Structure and Pre-Large Itemsets

Incremental data mining has been discussed widely in recent years, as it has many practical applications, and various incremental mining algorithms have been proposed. Hong et al. proposed an efficient incremental mining algorithm for handling newly inserted transactions by using the concept of pre-large itemsets. The algorithm aimed to reduce the need to rescan the original database and also cut maintenance costs. Recently, Lin et al. proposed the Pre-FUFP algorithm to handle new transactions more efficiently, and make it easier to update the FP-tree. However, frequent itemsets must be mined from the FP-growth algorithm. In this paper, we propose a Pre-FUT algorithm (Fast-Update algorithm using the Trie data structure and the concept of pre-large itemsets), which not only builds and updates the trie structure when new transactions are inserted, but also mines all the frequent itemsets easily from the tree. Experimental results show the good performance of the proposed algorithm

A study on incremental mining of frequent patterns

Data generated from both the offline and online sources are incremental in nature. Changes in the underlying database occur due to the incremental data. Mining frequent patterns are costly in changing databases, since it requires scanning the database from the start. Thus, mining of growing databases has been a great concern. To mine the growing databases, a new Data Mining technique called Incremental Mining has emerged. The Incremental Mining uses previous mining result to get the desired knowledge by reducing mining costs in terms of time and space. This state of the art paper focuses on Incremental Mining approaches and identifies suitable approaches which are the need of real world problem.Keywords: Data Mining, Frequent Pattern, Incremental Mining, Frequent Pattern Minung, High Utility Mining, Constraint Mining