Integrating data mining and rough set for customer group-based discovery of product configuration rules

Shao, Xinyu; Wang, Z.-H; Li, P.-G; Feng, Cheng

doi:10.1080/00207540600675777

Cited by 79 publications

(27 citation statements)

References 27 publications

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“…By reduct calculation and knowledge extraction, rough sets data analysis approaches can establish the mapping relationship between equivalent classes of input space and decision classes in information system, then based on it, the decision model of classification problem can be built from original data. Recently rough sets has been extensively applied in data mining [2,3], knowledge discovery [4,5], uncertain reasoning [6,7], granular computing [8,9], and so on.…”

Section: Introductionmentioning

confidence: 99%

Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Zhang

Wang

2009

Neural Process Lett

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Based on bottom-up fuzzy rough data analysis, a new rough neural network decision-making model is proposed. Through supervised Gaustafason-Kessel (G-K) clustering algorithm, proper fuzzy clusters are found to partition the input data space. At the same time cluster number is searched by monotone increasing process. If the cluster number matches with that exactly exist in data sets then excellent fuzzy rough data modeling (FRDM) model can be built. And by integrating it with neural network technique, corresponding rough neural network is constructed. Our method overcomes the defects of conventional top-down based rough logic neural network (RLNN) method, and it also achieves adaptive learning ability and comprehensive soft decision-making ability compared with FRDM model. The experiment results indicate that our method has stronger generalization ability and more compact network structure than conventional RLNN.

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Section: Introductionmentioning

confidence: 99%

Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Zhang

Wang

2009

Neural Process Lett

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“…Shao et al [153] proposed a data mining based architecture to discover customer group based configuration rules in configuration design. The association rule mining based on apriori algorithm was used to get the association rules between of clusters of products specifications and configuration alternatives.…”

Section: Association In Manufacturingmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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“…It is capable of dealing with qualitative or imprecise inputs from customers and designers. Numerous publications and research works indicate that fuzzy set analysis is a useful tool in decision-making problems with multiple goals or criteria (Shao et al 2006;Zha et al 2005). Moreover, fuzzy set analysis is appropriate in making the best selection among several alternatives under defined criteria which are represented in fuzzy terms.…”

Section: Introductionmentioning

confidence: 99%

An optimum design selection approach for product customization development

2010

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Consumer preferences and information on product choice behavior can be of significant value in the development processes of innovative products. In this paper, product customization evaluation and selection model is introduced to support imprecision inherent of qualitative inputs from customers and designers in the decision making process. Focusing on customer utility generation, an optimum design selection approach based on fuzzy set decision-making is proposed, where design attributes priority is identified from customer preferences using an analytical hierarchy process. A multi-attribute analysis diagram is developed to visualize the preference of each attribute from the expert's group decision. Conjoint analysis is used in the product customization to focus on customer utility generation in terms of multiple criteria. The use of the decision-making method is illustrated with a case example that highlights the utility of the proposed method.

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Integrating data mining and rough set for customer group-based discovery of product configuration rules

Cited by 79 publications

References 27 publications

Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

Data mining in manufacturing: a review based on the kind of knowledge

An optimum design selection approach for product customization development

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