Study On Key Issues In Association Rule Mining Based On Concept Lattice Model

Association rule mining is an important branch of data mining, the research and applications on which have been already made significant progress. However, due to the immense data, heterostructure and multiplicity of data types, complicated data structures in databases are available in the real world, those have made association rule mining face a lot of challenges, therefore, the complete concept lattice model is introduced to the study of association rule mining in the dissertation, and some studies are made on concept lattice model, algorithms, and data reduction etc. Contributions of the dissertation are as follows:1. On the basis of study on the relationship of concept lattice model and frequent itemsets, the itemsets representation and their solution methods are proposed. The study shows that each itemset should be presented as the intents or sub-intents of one concept in the concept lattice, thus it is able to completely describe these itemsets, similarly, many itemsets are derived from one concept through the relations among concepts in the concept lattice, consequently, the number of concepts in the concept lattice is reduced more markedly than that of itemsets in transaction databases, which can be used in KDD effectively without any valid information loss, Besides, pruned concept lattice, derived form concept lattice by deleting the infrequent concepts, not only cuts efficiently down the sizes of databases, but also is convenient to association rule mining.2. The study is made on the solution to association rule mining algorithm based on the pruned concept lattice, the build algorithms of sequential and synchronal pruned concept lattice is proposed, the former constructs concept lattice by the records insertion on the basis of Godin build algorithm, which need scan the databases only once, finishes the pruning by the Apriori properties after having completed the concept lattice construction, whereas, the latter dynamically constructs concept lattice by the attributes insertion, which implements the pruning by the Apriori properties in the course of concept lattice construction, these concept lattice build algorithms have their own characteristics and applicabilities. Association rule mining based on pruned concept lattice reduces the search space, and the efficiency of association rules mining algorithm is improved.3. Generalization with multi-level and multi-attribute is studied. There are many differences among attribute values in the practical databases, thus data reduction should be done necessarily. However, there are subjective limits in typical generalization methods, which maybe make an impact on valued model discovery, therefore, attribute-oriented induction algorithm based on concept lattice, one of multi-attributes generalization model, is presented, which can carry out the generalization with multi-level and multi-attribute through concepts ascension in the concept lattice.Compared with Attribute-Oriented Induction (AOI) algorithm, proposed by Han Jiawei, the former can not only perform the same task of the latter, but also carry out the generalization with multi-level and multi-attribute, but the generalization paths are not one and only, and the proper generalization paths and thresholds can be easily found in the concept lattice, then the required reasonable results can be obtained so that thses proper granule association rules can be mined.