Acquisition Method of User Requirements for Complex Products Based on Data Mining

Abstract

The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility.

How Could Consumers’ Online Review Help Improve Product Design Strategy?

“…Huang et al [36] applied three algorithms, namely term frequency-inverse document frequency (TF-IDF), Simpson's diversity index (SDI), and Latent Dirichlet Allocation (LDA), to explore the design trends in Taiwanese design journals since the 1960s, of which the results reveal the current and future trends in the academic community, providing a reference for studying design histories in other regions of the world. Hao et al [37] proposed a user demand acquisition method based on patent data mining. This method constructs a patent data knowledge base and applies the Latent Dirichlet Allocation topic model and K-means algorithm to cluster patent text data, enabling the mining of key functional requirements for product development.…”

A Review Report on Requirements Analysis with Data Mining

“…It includes understanding the system's goals, functions, and constraints, as well as eliciting, analyzing, and prioritizing requirements to ensure that the final solution meets the desired objectives [6]. The advancements in cloud computing, industrial internet, machine learning, and other emerging technologies have caused a huge influx of data from a variety of heterogeneous sources [4]. Because of the huge amount of heterogeneous data, classic methodologies of the software requirements analysis processes fails to capture efficiently capture customer requirements for modern-day software systems.…”

“…A variety of data mining techniques have been researched for software requirement engineering. [4], [11] and [12] utilized Latent Dirichlet allocation (LDA) topic modeling to capture software requirements from big data systems. [1], [2], and [6] have employed natural language processing, sentiment analysis, and other data mining techniques for gathering software requirements from online data sources.…”

A Review Report on Requirements Analysis with Data Mining

“…It includes understanding the system's goals, functions, and constraints, as well as eliciting, analyzing, and prioritizing requirements to ensure that the final solution meets the desired objectives [6]. The advancements in cloud computing, industrial internet, machine learning, and other emerging technologies have caused a huge influx of data from a variety of heterogeneous sources [4]. Because of the huge amount of heterogeneous data, classic methodologies of the software requirements analysis processes fails to capture efficiently capture customer requirements for modern-day software systems.…”

“…A variety of data mining techniques have been researched for software requirement engineering. [4], [11] and [12] utilized Latent Dirichlet allocation (LDA) topic modeling to capture software requirements from big data systems. [1], [2], and [6] have employed natural language processing, sentiment analysis, and other data mining techniques for gathering software requirements from online data sources.…”