Customer Requirements Mapping Method Based on Association Rule Mining for Mass Customization

Customer requirements analysis is the key step for product variety design of mass customiza-tion(MC). Quality function deployment (QFD) is a widely used management technique for understanding the voice of the customer (VOC), however, QFD depends heavily on human subject judgment during extracting customer requirements and determination of the importance weights of customer requirements. QFD pro-cess and related problems are so complicated that it is not easily used. In this paper, based on a general data structure of product family, generic bill of material (GBOM), association rules analysis was introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, and decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin was used to illustrate the proposed method.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Herbal medicine prescribing patterns from contemporary famous old TCM doctors for treating coronary heart disease: an analysis based on data mining

To analyze the herbal medicine prescribing patterns of contemporary famous old traditional Chinese medicine doctors in treating coronary heart disease,based on data mining technology,so as to provide useful insights into the clinical practice.Methods:Databases,including Medline(January 1966 to December 2019),Wanfang(January 1982 to December 2019),VIP Database(January 1989 to December 2019),CNKI(January 1979 to December 2019),CBMdisc(January 1978 to December 2019),and Classic Case Collection from Contemporary Famous Old Traditional Chinese Medicine Doctors were searched,and 224 eligible studies involving 416 patients were entered into the case study database after data processing.Frequency analysis and association rule analysis were used to investigate the prescribing patterns of contemporary famous old traditional Chinese medicine doctors in treating coronary heart disease.Results:In total 290 kinds of Chinese herbal drugs and 19 core drugs were used in the cases studied.The most commonly used categories were“Qi-Tonifying Drugs”,“Blood-Activating Drugs”,and“Phlegm-Eliminating Drugs”.The association rule analysis identified 14 commonly used herbal pairs,19 three-drug combinations,and 1 four-drug combination.Conclusion:Contemporary famous old traditional Chinese medicine doctors considered warming heart yang as an extremely important approach to treat coronary heart disease based on Zhang Zhongjing’s treatment for chest painful obstruction caused by“Inactivity of Chest Yang”(blockade of phlegm turbidity).Both symptoms and root causes were addressed in the formulas prescribed by these doctors.“Tonifying Qi,Nourishing Yin,Activating Blood,and Eliminating Phlegm”were the most commonly used therapeutic methods for patients with coronary heart disease.

A data mining approach to characterize road accident locations

Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. Theassociation rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.

Exploration on the acupoint selection rule in modern literature for chronic gastritis treated with acupuncture and moxibustion in recent 10 years

Objective To summarize the acupoint selection rule for chronic gastritis treated with acupuncture and moxibustion so as to provide a certain evidence for clinical practice and scientific research.Methods By searching journal literature on chronic gastritis treated with acupuncture and moxibustion in recent 10 years,with data mining,the acupoints screened from literature were analyzed.Results A total of 803 articles were included finally.Conception vessel,stomach meridian and bladder meridian were mostly selected,the acupoints were selected from the abdomen,the thigh/crus,the back and the arms.The top 5 aucpoints with high frequency were Zúsānǐ(足三里ST36),Zhōngwǎn(中脘CV12),Wèishū(胃俞BL21)(332),Píshū(脾俞BL20)and Nèiguān(内关PC6).The top 5 acupoint combinations with high frequency included CV12 combined with ST36,BL21 with CV12,BL21 with ST35 and BL20 with CV12.The mostly used specific points were front-mu point,he-sea point,crossing point and back-shu point.Conclusion In treatment of chronic gastritis with acupuncture and moxibustion,conception vessel,stomach meridian and bladder meridian are particularly selected in combination and the specific points with duplicate effect are mostly selected,especially focusing on the application of front-mu point.