| Knowledge is the most valuable assets of a manufacturing enterprise. Data mining can extract valuable knowledge from all kinds of manufacturing data, which has promoted enormously the development of manufacturing technology and manufacturing mode. Association rules is one of the most important data mining technologies which can effectively find the relationship between data items. And the expressions of association rules are concise and easy to understand and explain. So association rules algorithm research has important theoretical significance and broad application prospect which has been a hot research field of data mining. In this paper, the key technologies of association rules and their application in product continual quality improvement have been studied deeply. The main innovation work is as follows:(1) For generating conditional FP-tree, FP-growth algorithm need scanning database twice. Thus FP-growth algorithm can't adapt to the characteristics of data in dynamic real-time database. Aiming at the limitations, this paper presents a novel FP array technology. The counts of frequent items are obtained directly from FP array, thus the first scan is omitted. An improved frequent itemsets mining algorithm and a closed frequent itemsets mining algorithm are presented which use the FP-tree data structure in combination with the FP array technology. Experimental evaluations show that the two algorithms have stable superior performance in running time, memory consumption and scalability aspects especially for the sparse database.(2) Apriori and FP-growth algorithm process all transactions in a batch way which can't adapt to the need to update association rules dynamically. This paper presents the concept of pre-frequent itemsets. Through an upper minimum support threshold and a lower minimum support threshold, pre-frequent itemsets are defined. On the basis of fast updated FP-tree algorithm(FUFP), this paper presents an improved algorithm based on pre-frequent itemsets which does not need to scan the original database untill the new transactions reach a certain amount. So it improves the efficiency of the update. Experimental evaluations show that the larger the size of the database, the more obvious the performance advantages of the algorithm.(3) Customer demand is the driving force behind the development of enterprises. The diversity of customer demands leads to the diversity of the questionnaire data type. But traditional association rules mining algorithm can't handle a variety of types of data. Aiming at the limitation, this paper first defines the various data type and mining rules mode, and presents to statistic the support counts of items by similarity degree. Then a novel method based on fuzzy set theory is presented to deal with all kinds of data types in a unified way. At last a fuzzy association rules based Apriori is presented which is applied to the analysis of survey data of electric bicycles.(4) Based on the above research work, this paper designs an information system for product continual quality improvement (ARMS) whose goal is to product high quality products with low cost and low consumption of resources and to improve customer satisfaction. The system integrates the process and quality data in different departments through the process quality language based on XML. Then on the basis of it, a mining algorithm which is proposed in this paper is used to find the relationship between the distributed process parameters combination and product quality problems, then the genetic algorithm is used to optimize these rules so that can help quality managers to adjust the settings of process parameters in order to facilitate continual quality improvement. |
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