An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm–based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm; the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm–based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm–based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm–based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm–based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

中文翻译：

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关联规则挖掘使用混沌引力搜索算法发现制造系统能力与产品特征之间的关系

一种有效的数据挖掘方法可以从可用的历史数据中自动提取制造能力和产品特征之间的关联规则，对于高效且具有成本效益的产品开发和生产至关重要。本文提出了一种基于混沌引力搜索算法的关联规则挖掘方法，用于发现制造系统能力与产品特征之间的隐藏关系。提取的规则将用于预测具有不同功能的新产品的各种机器的能力需求。我们使用两种策略将混沌结合到引力搜索算法中：一种策略是将混沌映射函数嵌入到引力搜索算法的引力常数中；另一种是利用混沌映射产生的序列，用随机数代替引力搜索算法的不同参数。为了提高基于混沌引力搜索算法的关联规则挖掘的适用性，进一步提出了一种新的重叠度量指示来消除那些无用的规则。所提出的方法相对简单且易于实现。由基于混沌引力搜索算法的关联规则挖掘生成的规则准确、有趣且用户易于理解。性能比较表明，基于混沌引力搜索算法的关联规则挖掘优于关联规则挖掘的其他常规方法（例如 Apriori）。实验结果表明，基于混沌引力搜索算法的关联规则挖掘能够发现制造系统能力与产品特征之间的重要关联规则。这将有助于支持规划人员和工程师进行新产品的设计和制造。