Classification Research Based On Constrained Frequent Pattern

Alumina clinker quality classification occupies an important position in the production of alumina. As the temperature of alumina clinker kiln depends on totally, the quality analysis of alumina clinker has a great impact in its yield and quality. Now the way of alumina clinker quality analysis not only has a serious threat to the safety and health of staff, but also has the problems of justify reliance on the experience and the lack of scientific.Constrained frequent pattern is an important research direction of data mining, and the mining of frequent patterns based on constraints can enhance the efficiency of data mining and make the process more targeted. In this paper we present a discovery algorithm of frequent pattern based on various time constraints, which uses the time constraints to reduce and divide the search space, and does not produce the candidate items. Due to the introduction of close time, it can guarantee the newest sequence patterns by using this algorithm.This paper studies the possibility of combining alumina clinker quality classification and constrained frequent pattern together. We also mine the the audio data by the presented algorithm, and analyze the characteristics of constrained frequent pattern. In the end, we propose the classification method of alumina clinker quality based on various time constraints, and put the Constrained frequent pattern into the Alumina clinker quality classification. The results not only improve the accuracy of clinker quality classification, but also apply the constrained frequent pattern into the domain of classification.