Research On Association Rule Mining Algorithm Based On Disjunction-free Sets

Data mining can be viewed as a result of the natural evolution of information technology.Data mining tools perform data analysis and may uncover important data patterns,contributing greatly to strategies. Data mining essentially aims at applications.As an important technology of data mining,association rule mining has been applied greatly to various fields,especially business field.As the growth of the data sets' size and complexity,it is crucial for us to study the association rule mining algorithm to ensure system's high-performance and strong applicability to data sets.Association rule mining is a two-step process.Find all frequent itemsets at first.Then generate strong association rules from the frequent itemsets.Research emphasizes particularly on the first step currently.The famous algorithms to find the frequent itemsets include Apriori and FP-growth.Many variations of these algorithms have been propsed that focus on improving various questions. An Apriori-like algorithm IHPD combines the advantages of many algorithms then improves the algorithm performance greatly and can be applied to more cases.IHPD is an algorithm based on condensed representation in essence. Algorithms based on condensed representation are based on the following idea.It is sufficient to extract a particular subset of the frequent pattern collection,such that we can regenerate from the subset the whole collection without costly scan of the original data and new support counting.IHPD combines all the advantages of IHP and DHP then applies them to condensed representation mining,which breaks the restriction that the algorithm can be efficient only in difficult cases.