Rong Jeng

Incremental maintenance of generalized association rules under taxonomy evolution

Mining association rules from large databases of business data is an important topic in data mining. In many applications, there are explicit or implicit taxonomies (hierarchies) for items, so it may be useful to find associations at levels of the taxonomy other than the primitive concept level. Previous work on the mining of generalized association rules, however, assumed that the taxonomy of items remained unchanged, disregarding the fact that the taxonomy might be updated as new transactions are added to the database over time. If this happens, effectively updating the generalized association rules to reflect the database change and related taxonomy evolution is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called IDTE and IDTE2, which can incrementally update the generalized association rules when the taxonomy of items evolves as a result of new transactions. Empirical evaluations show that our algorithms can maintain their performance even for large numbers of incremental transactions and high degrees of taxonomy evolution, and are faster than applying contemporary generalized association mining algorithms to the whole updated database.

Mining Association Rules with Ontological Information

The problem of mining association rules incorporated with domain knowledge has been studied recently. Previous work was conducted individually on two types of knowledge, classification and composition. In this paper, we revisit this problem from a more unified viewpoint. We consider the problem of mining association rules with ontological information that presents not only classification but also composition relationship. Two effective algorithms are proposed with empirical evaluation displayed.

Efficient remining of generalized association rules under multiple minimum support refinement

Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in the data mining community. In real applications, however, the work of discovering interesting association rules is an iterative process; the analysts have to continuously adjust the constraint of minimum support to discover real informative rules. How to reduce the response time for each remining process thus becomes a crucial issue. In this paper, we examine the problem of maintaining the discovered multi-supported generalized association rules when the multiple minimum support constraint is refined and propose a novel algorithm called RGA_MSR to accomplish the work. By keeping and utilizing the set of frequent itemsets and negative border, and adopting vertical intersection counting strategy, the proposed RGA_MSR algorithm can significantly reduce the computation time spent on rediscovery of frequent itemsets and has very good performance.

Dynamic Mining of Multi-supported Association Rules with Classification Ontology

One of the predominant techniques used in the area of data mining is association rule mining. In real world, data mining analysts usually are confronted with a dynamic environment; the database would be changed over time, and the analysts may need to set different support constraints to discover real informative rules. Efficiently updating the discovered association rules thus becomes a crucial issue. In this paper, we consider the problem of dynamic mining of association rules with classification ontology and with non-uniform multiple minimum supports constraint. We investigate how to efficiently update the discovered association rules when there is transaction update to the database and the analyst has refined the support constraint. A novel algorithm called DMA_CO is proposed. Experimental results show that our algorithm is 14% to 80% faster than applying generalized associations mining algorithms to the whole updated database.

Maintenance of generalized association rules under transaction update and taxonomy evolution

Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model in data mining. Earlier work on mining generalized association rules ignore the fact that the taxonomies of items cannot be kept static while new transactions are continuously added into the original database. How to effectively update the discovered generalized association rules to reflect the database change with taxonomy evolution and transaction update is a crucial task. In this paper, we examine this problem and propose a novel algorithm, called IDTE, which can incrementally update the discovered generalized association rules when the taxonomy of items is evolved with new transactions insertion to the database. Empirical evaluations show that our algorithm can maintain its performance even in large amounts of incremental transactions and high degree of taxonomy evolution, and is more than an order of magnitude faster than applying the best generalized associations mining algorithms to the whole updated database.

Incremental Maintenance of Generalized Multi-supported Association Rules under Transaction Update and Taxonomy Evolution

Mining generalized association rules has been recognized as a very important topic in data mining. Earlier work on mining generalized association rules ignores the fact that the taxonomy of items would be changed while new transactions are continuously added into the database. In our previous paper, we have proposed a method to solve this problem with uniform minimum support; however, a uniform minimum support assumption would obstruct the discovery of associations on some high value or new items that are more interesting but much less supported than general trends. In this paper, we examine this problem and propose a novel algorithm, called MMAITTE, which can incrementally update the discovered generalized association rules with multiple minimum supports when the taxonomy of items is evolved with incremental transactions. Experimental results show that our algorithm can maintain its performance even in large amounts of incremental transactions and high degree of taxonomy evolution, and is faster than applying the contemporary generalized association mining algorithms to the whole updated database.