A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

2004 ◽  
Vol 4 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Carol J. Romanowski , ◽  
Rakesh Nagi
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Research Direction ◽  
Design Constraints ◽  
Variant Design ◽  
Data Mining Approach ◽  
Design Activities ◽  
Using Data ◽  
New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

scholarly journals Predicting COVID-19 Incidence Using Data Mining Techniques: A case study of Pakistan

2020 ◽  
Vol 11 (4) ◽  
pp. 168-184
Author(s):  
Saba NOOR ◽  
◽  
Waseem AKRAM ◽  
Touseef AHMED ◽  
Qurat-ul-Ain Qurat-ul-Ain ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Wuhan City ◽  
Data Mining Approach ◽  
Life Threatening ◽  
Precautionary Measures ◽  
The Government ◽  
Predictive Data Mining ◽  
Using Data

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic.

Data Mining and Fuzzy Clustering to Support Product Family Design

2006 ◽  
Author(s):  
Seung Ki Moon ◽  
Soundar R. T. Kumara ◽  
Timothy W. Simpson
Keyword(s):  
Data Mining ◽  
Mass Customization ◽  
Product Family ◽  
Knowledge Based ◽  
Product Function ◽  
Fuzzy C Means Clustering ◽  
Large Product ◽  
Functional Features ◽  
Using Data

In mass customization, data mining can be used to extract valid, previously unknown, and easily interpretable information from large product databases in order to improve and optimize engineering design and manufacturing process decisions. A product family is a group of related products based on a product platform, facilitating mass customization by providing a variety of products for different market segments cost-effectively. In this paper, we propose a method for identifying a platform along with variant and unique modules in a product family using data mining techniques. Association rule mining is applied to develop rules related to design knowledge based on product function, which can be clustered by their similarity based on functional features. Fuzzy c-means clustering is used to determine initial clusters that represent modules. The clustering result identifies the platform and its modules by a platform level membership function and classification. We apply the proposed method to determine a new platform using a case study involving a power tool family.

scholarly journals Using Data Mining Methods for Predicting Sequential Maintenance Activities

2018 ◽  
Vol 8 (11) ◽  
pp. 2184 ◽  
Author(s):  
Sadok Rezig ◽  
Zied Achour ◽  
Nidhal Rezg
Keyword(s):  
Data Mining ◽  
Spare Parts ◽  
Threshold Values ◽  
Data Mining Approach ◽  
History Of ◽  
Maintenance Activities ◽  
Using Data ◽  
Subsequent Failure ◽  
Sequential Maintenance

A data mining approach is integrated in this work for predictive sequential maintenance along with information on spare parts based on the history of the maintenance data. For most practical problems, the simple failure of one part of a given piece of equipment induces the subsequent failure of the other parts of said equipment. For example, it is frequently observed in mining industries that, like many other industries, the maintenance of conventional equipment is carried out in sequence. Besides, depending on the state of parts of the equipment, many parts can be consumed and replaced. Consequently, with a group of spare parts consumed sequentially in various maintenance activities, it is possible to discover sequential maintenance activities. From maintenance data with predefined support or threshold values and spare parts information, this work determines the sequential patterns of maintenance activities. The proposed method predicts the occurrence of the next maintenance activity with information on the consumed spare parts. An industrial real case study is presented in this paper and it is well-noticed that our experimental results shed new light on the maintenance prediction using data mining.

scholarly journals ANALYSIS OF THESIS ADVISING PATTERN IN MASTER PROGRAM ON FACULTY OF AGRICULTURE IPB USING DATA MINING APPROACH

Edulib ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 194
Author(s):  
Lilis Syarifah ◽  
Imas Sukaesih Sitanggang ◽  
Pudji Muljono
Keyword(s):  
Data Mining ◽  
Upland Rice ◽  
Future Research ◽  
Rule Mining ◽  
Related Item ◽  
Text Documents ◽  
Master Program ◽  
R Language ◽  
Data Mining Approach ◽  
Using Data

The thesis is student study report which is accomplished as a requirement of graduation for Master program. Selecting study’s topic and advisors influence implementation of the study. Therefore, study’s topic is able to improve academic institution quality, however a large number of thesis documents on the repository cause difficulty to get information related to advisor’s expertness and the frequent or rare topic is former studied. Association rule mining can be used to mine information on the related item. This study aims to analyze advising patterns system in Master program on Agriculture based on supervisors and their topic research on metadata thesis of IPB repository and text documents of summary using data mining approach. The datas were collected from the repository of Bogor Agricultural University website and processed using R language programming. Pattern result of the reseach were that the most popular association on supervisor was occurred at support value of 0.00793 or equivalent to 7 theses and four popular topics were Botanical insecticide, Global warming, Upland Rice, and Land Use Change. The analysis result could be useful information to be reference or suggest future research or appropriate supervisor among agricultural.

scholarly journals Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

2020 ◽  
Keyword(s):  
Data Mining ◽  
Customer Segmentation ◽  
Food Distribution ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Marketing Activities ◽  
Using Data ◽  
Production Company

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

Sign in / Sign up

Export Citation Format

Share Document