The Study Of Lattice Union Algorithm And Match Model In Concept Lattice Theory

With the wide application of computer and network information technology in various fields, theway for getting information is becoming more and more intelligent. How to obtain important knowledgefrom the relevant information is one of the current key issues. Concept lattice theory focused on solvingthe problem how to do with the data and information efficiently, and discovering the availableknowledge and rules. Aiming to the union algorithm and match model in current concept lattice study,this paper deeply studies the efficient algorithm of concept lattice union and accurate similarity modelof concept lattice.The main work of this paper is as follows:In existing literatures, the algorithms of concept lattice union are mostly based on a one-way union.A new algorithm of lattice union is proposed in this paper, which it is not based on a one-way, but fromtop to bottom in two different ways to union the concept lattice progressively at the same time. Ouralgorithm does union by the ascending and descending order of the extension and intention of theconcept, and finishes the union of the whole concept lattice in the middle. Examples and analysis resultsshow that compared with other algorithms, the new algorithm improves the efficiency of the conceptlattice union obviously and keeps the original structured information as well. Therefore, it can beapplied practically in the concept lattice structure of large-scale distributed data processing.Aiming to the another problem that the existing concept lattice matching model mostly onlyconsiders the structural information of the concept while ignores the semantic information between twoconcepts, or only considers the semantic information without using the structural information of theconcept, we proposes a new concept similarity calculation model in this paper. Firstly, establish theformal context and concept lattice by the partial order relation between concept features. Then obtainthe irreducible cross between concepts in structural levels, and calculate the similar to degrees betweenconcepts by quantifying the semantic relationships of the elements in the collection. The example showsthat this new concept of similarity calculation model improved the performance of the concept matchingobviously. Therefore, it can be applied practically in the semantic web, information retrieval, digitallibraries and other relative application areas.