Relevant bibliographies by topics / Product Clustering / Journal articles

To see the other types of publications on this topic, follow the link: Product Clustering.

Journal articles on the topic 'Product Clustering'

Author: Grafiati

Published: 18 April 2022

Last updated: 18 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Product Clustering.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

Pepall, Lynne. "Market Demand and Product Clustering." Economic Journal 100, no. 399 (1990): 195. http://dx.doi.org/10.2307/2233603.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Zhang, Lei, Zhi Feng Liu, and Hong Bao. "Customer Clustering for Product Green Design." Applied Mechanics and Materials 130-134 (October 2011): 3763–66. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3763.

Full text

Abstract:

The characteristic of product’s life-cycle customer environment requirements is analyzed. A customer clustering methode for green design is proposed. Conjoint analysis method is used to obtain individual customer’s environment requirements and system-clustering method is used to cluster customers in accordance with their environment requirements preference. Green product customer clustering is realized. Thus, successive development of green product can be supported. Green design oriented customer clustering of soybean milk maker is illustrated to exemplify the method.

APA, Harvard, Vancouver, ISO, and other styles

Astuti, Rini, Nining Rahaningsih, Umi Hayati, Cep Lukman Rohmat, and Nana Suarna. "Implementation of Fuzzy C-Means Algorithm with Optimized Parameter Grid for Clustering Electronic Product Sales." East Asian Journal of Multidisciplinary Research 2, no. 4 (2023): 1647–60. http://dx.doi.org/10.55927/eajmr.v2i4.3929.

Full text

Abstract:

The sales of electronic products have increased rapidly over the past few years. However, grouping products based on certain criteria is still an unresolved issue. Therefore, research is needed to develop more accurate clustering methods. Currently, the problem with electronic product clustering using the k-means method still has limitations, such as sensitivity to initial centroid values and inability to handle overlap between clusters. Therefore, research is needed to optimize the grid parameter of the Fuzzy C-Means algorithm to produce more accurate clustering. The purpose of this study is to implement the Fuzzy C-Means algorithm with optimized grid parameters to cluster electronic product sales more accurately. The method used in this study is an experimental research method. Electronic product sales data were obtained from specific stores, and the Fuzzy C-Means algorithm with optimized grid parameters was applied to cluster electronic products. The results show that implementing the Fuzzy C-Means algorithm with optimized grid parameters can produce more accurate electronic product clustering compared to the k-means method. By using optimized grid parameters, the Fuzzy C-Means algorithm can handle overlap between clusters and produce more stable centroids with a Dbi accuracy value of 0.510 for Numerical Measure and 0.611 for Mixed Measure.

APA, Harvard, Vancouver, ISO, and other styles

Chukhray, Nataliya, Kateryna Yarmola, and Andrii Chukhrai. "Tourism product consumers clustering for developing the tailored marketing mix." Innovative Marketing 21, no. 1 (2025): 281–95. https://doi.org/10.21511/im.21(1).2025.23.

Full text

Abstract:

The constant change in consumer preferences requires adjusting sales strategies according to the consumer’s current needs. The study aims to cluster the consumers of tourism products based on the factors influencing their decision-making process when choosing tourism products and to classify them according to the marketing mix. The study is based on the analysis of data from 196 respondents in the Lviv region, collected through an online survey using Google Forms in the first decade of 2023. The sample is representative, as it was calculated considering the population of the Lviv region aged 16 and above, ensuring the results’ reliability and relevance. The results revealed that representatives of each cluster are, on average, willing to spend up to 10,000 UAH per person during their vacation. In the decision-making process regarding the purchase of components of a tourist product, accommodation holds the most significant importance for representatives of the first and second clusters (4.51 and 3.27, respectively), insurance is the most important for the third cluster (4.71), and food is the priority for the fourth cluster (2.54). The decisive components of tourist services and risks for all clusters include up-to-date information about the vacation destination and pandemics/diseases, although the significance of their influence varies. Additionally, the clusters differ regarding the elements of place and promotion of tourist products. The results demonstrate that the marketing mix elements vary across clusters despite certain similarities in respondents’ assessments.

APA, Harvard, Vancouver, ISO, and other styles

Quintana, Fernando A., and Pilar L. Iglesias. "Bayesian clustering and product partition models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 2 (2003): 557–74. http://dx.doi.org/10.1111/1467-9868.00402.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Gao, Fei, Gang Xiao, and Jiu-jun Chen. "Product interface reengineering using fuzzy clustering." Computer-Aided Design 40, no. 4 (2008): 439–46. http://dx.doi.org/10.1016/j.cad.2007.12.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Mehlstäubl, Jan, Christoph Pfeiffer, Ralf Kraul, Felix Braun, and Kristin Paetzold-Byhain. "METHODICAL APPROACH TO CLUSTER CONFIGURATIONS OF PRODUCT VARIANTS OF COMPLEX PRODUCT PORTFOLIOS." Proceedings of the Design Society 3 (June 19, 2023): 2645–54. http://dx.doi.org/10.1017/pds.2023.265.

Full text

Abstract:

AbstractCompanies are increasingly struggling to manage their complex product portfolios. Since they do not fully understand the complexity, intelligent solutions are required. Emerging technologies and tools offer new ways to deal with existing problems. With the help of clustering, similarities between product variants can be identified automatically, and complexity can be systematically reduced. This article aims to develop a methodological approach to identify correlations between product variants in complex product portfolios automatically by using clustering algorithms. The approach includes the systematic cleaning and transformation of product portfolio data. In addition, a guide for algorithm selection and evaluation of clustering results is presented. As the last step, the results are systematically analysed and visualised. To validate the methodical approach, it is applied to a real-world data set of a commercial vehicle manufacturer and the usefulness of the results is confirmed in an expert workshop.

APA, Harvard, Vancouver, ISO, and other styles

Wallstrom, Garrick L., and William R. Hogan. "Unsupervised clustering of over-the-counter healthcare products into product categories." Journal of Biomedical Informatics 40, no. 6 (2007): 642–48. http://dx.doi.org/10.1016/j.jbi.2007.03.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Diwangkara, Taufaldisatya Wijatama, Ulfi Saidata Aesyi, and Netania Indi Kusumaningtyas. "Rekomendasi Posting Promosi pada Sosial Media Berdasarkan Pengelompokan Hasil Penjualan Produk (Studi Kasus: Maula Hijab)." Teknomatika: Jurnal Informatika dan Komputer 14, no. 2 (2021): 76–85. http://dx.doi.org/10.30989/teknomatika.v14i2.1096.

Full text

Abstract:

Maula Hijab is an MSME (Small and Micro Medium Enterprises) located in Sidomoyo, Godean District, Sleman Regency, Yogyakarta Special Region Province that sells Muslim clothing products. Maula Hijab sells its products directly and through marketplace platforms such as Shopee, Lazada, and Tokopedia. In addition, Maula Hijab promotes its products through social media, one of which is Instagram. Social media is used to promote Maula Hijab products, but there is a decrease in the number of viewers reached by the Maula Hijab Instagram account. In addition, a decline in sales of Maula Hijab was found. Therefore, it is necessary to analyze the level of product promotion performance on Instagram on product sales. To analyze the two data, the Data Mining technique used in this study is K-Means Clustering. The K-Means Clustering algorithm is used to group, classify, or group a set of objects based on their attributes or features into a number of similar groups called clusters. This study aims to provide recommendations for promotion of Maula Hijab products using the K-Means Clustering algorithm. This study uses the K-Means Clustering method. The final result of this research is that 3 product clusters are produced, namely product clusters that are recommended to be promoted more often, product clusters that can be re-promoted, and product clusters that have good promotions. The recommendation system built can run to retrieve Instagram data and process the data to produce output in the form of product promotion recommendations.

APA, Harvard, Vancouver, ISO, and other styles

Zhang, Na, Wei Xu, and Yong Tan. "Multi-attribute hierarchical clustering for product family division of customized wooden doors." BioResources 18, no. 4 (2023): 7889–904. http://dx.doi.org/10.15376/biores.18.4.7889-7904.

Full text

Abstract:

To improve the production system for customized wooden doors and to gain research and development efficiency, this paper proposed the feasibility of using hierarchical clustering algorithms to cluster a company’s customized wooden door products and its application to rational product family architecture. The particular use of multi-attribute feature data to locate products and the integration of image data into the database can make the original hierarchical clustering more compatible and adaptable for application to customized wooden doors. The preprocessed data was analyzed by clustering to obtain the clustering results and similarity relationships. Hierarchical clustering results were uneven and not entirely interpreted. However, the internal order structure of clusters and the clustering process could be clearly observed, and the distance hierarchical relationship between the products could be obtained, which was beneficial to the division of the product architecture. The results illustrated that processing using hierarchical clustering of multi-attribute data is feasible for optimizing customized wooden door product systems. In addition, the product architecture, product coding rules, and front-end development process were established to improve standardization and research and development efficiency. There is still great potential for developing the custom wooden door category in custom furniture companies.

APA, Harvard, Vancouver, ISO, and other styles

Firdausi, Faiz, Eko Afrianto, and Ferry Wiranto. "Pastry Sales Data Clustering with K-Means Clustering Approach for Product Grouping." INSIDE - Jurnal Sistem Informatika Cerdas 1, no. 1 (2023): 36–41. http://dx.doi.org/10.31967/inside.v1i1.887.

Full text

Abstract:

In the business world that is run by many people today, we are required to always develop our business so that it always develops to make a profit. To achieve this, there are several things that can be done, namely by increasing product quality, adding product types, and reducing company operational costs by using company data analysis. Mr Dosi's Pastry Shop is a shop engaged in the sale of packaged dry food. Data on the purchase of MSME packaged pastry stock was not taken into account, which then led to a buildup of packaged food which made sales turnover less effective. Packaged pastries are a type of food that is packaged in packs per kilogram. Efforts are needed to find out the causes of sales that have decreased so that sales targets cannot be achieved. Related to the process of grouping the data, a data mining technique is used to perform clustering. This clustering process can be done using K-Means Clustering. This algorithm performs grouping of data sets into a predetermined cluster which aims to form separate data groups that have similarities. The results of the clustering of product sales levels are then used as a reference in warehouse management. Based on the grouping results, the grouping with 2 clusters is the most optimal grouping result with the smallest Davies-Bouldin Index (DBI) value, namely 0.125.

APA, Harvard, Vancouver, ISO, and other styles

Legito, Fegie Yoanti Wattimena, Yulianto Umar Rofi'i, and Munawir. "E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering." International Journal Software Engineering and Computer Science (IJSECS) 3, no. 2 (2023): 162–73. http://dx.doi.org/10.35870/ijsecs.v3i2.1527.

Full text

Abstract:

This research proposes and implements an e-commerce product recommendation system that combines Case-Based Reasoning (CBR) and K-Means Clustering algorithms. The main aim of this research is to provide more personalized and relevant product recommendations to e-commerce users. The CBR approach leverages users' transaction history to provide customized recommendations, whereas K-Means Clustering groups users with similar preferences increase the relevance of recommendations. This study assesses the effectiveness of the system by conducting a comprehensive evaluation by comparing system recommendations with actual user preferences. The results of this study reveal that the combined approach of CBR and K-Means Clustering can improve the performance of e-commerce product recommendations, ensure the accuracy of recommendations, and produce a more satisfying shopping experience for users. Although there are limitations in terms of the dataset used and the choice of algorithm parameters, this research makes an important contribution in developing a more adaptive and personalized recommendation system for e-commerce platforms.

APA, Harvard, Vancouver, ISO, and other styles

Pons, Marc, Andrea Bikfalvi, and Josep Llach. "Clustering product innovators: a comparison between conventional and green product innovators." International Journal of Production Management and Engineering 6, no. 1 (2018): 37. http://dx.doi.org/10.4995/ijpme.2018.8762.

Full text

Abstract:

<p>This paper aims at analysing firms implementing new products. Based on a cluster analysis, three types of manufacturers have been identified representing different types of product innovators according to the competitiveness factors important for their business, environmentally sensitive new products, and a performance indicator, such as the share of turnover from new products.</p>

APA, Harvard, Vancouver, ISO, and other styles

Niko, Niko Suwaryo, Arif Rahman, Dewi Marini Umi Atmaja, and Amat Basri. "Klasterisasi Stok Produk Retail Untuk Menetukan Pergerakan Kebutuhan Konsumen Dengan Algoritma K-Means." Bulletin of Information Technology (BIT) 4, no. 3 (2023): 306–12. http://dx.doi.org/10.47065/bit.v4i3.736.

Full text

Abstract:

− Retail product clustering is a product arrangement that is adjusted to the flow of placement or this layout is more suitable for product placement according to standards. Utilization of existing data through the clustering method approach can be applied in analyzing product grouping of data on availability and inventory of goods in warehouses so that it can provide knowledge and information. The clustering method is processed using the K-Means algorithm, where the results also show a new insight, namely grouping products based on 3 clusters. Cluster 1 is a product category with low availability or Low, namely 939 out of 1000 availability categories based on the number of products tested, then cluster 2 is a product category with medium or Medium availability, namely 51 out of 1000 availability categories based on the number of products tested, and finally cluster 3 is a product category with fairly high availability or High, namely 10 out of 100 availability categories based on the number of products tested. Tests using Rapid Miner tools can also produce similar insights, namely that each cluster has cluster group members according to manual calculations such as Cluster_0 in Rapid Miner has 51 cluster members representing the Medium cluster, Cluster_1 has 939 cluster group members representing the Low cluster, and Cluster_2 has 10 cluster members corresponding to the cluster representation High.

APA, Harvard, Vancouver, ISO, and other styles

Rui, Ni. "Application of Cluster Analysis in Optimizing Financial Product Recommendation System." Advances in Economics, Management and Political Sciences 157, no. 1 (2025): 121–26. https://doi.org/10.54254/2754-1169/2025.ab22678.

Full text

Abstract:

A financial product recommendation system is an important tool for financial institutions in financial services to enhance customer experience and satisfaction. Such systems provide personalized recommendations, assisting users in identifying and selecting appropriate financial products. However, traditional recommendation methods, including collaborative filtering and content-based recommendation, frequently suffer from limited personalization capabilities due to challenges like sparse data and user cold-start problems. This study investigates the application of cluster analysis in financial product recommendation systems and the role of various clustering algorithms (e.g., K-means, DBSCAN, and hierarchical clustering) in customer segmentation and personalized recommendations and evaluates their advantages and challenges in optimizing recommendation strategies. The results show that clustering analysis can effectively group users and products, significantly improving product matching and, thus, recommendation accuracy and customer satisfaction. In addition, this paper discusses the main challenges of applying clustering techniques to financial recommendation systems, such as ensuring the scalability of clustering models and accurately evaluating validation clustering results.

APA, Harvard, Vancouver, ISO, and other styles

Andriyani, Fitri, and Yan Puspitarani. "Performance Comparison of K-Means and DBScan Algorithms for Text Clustering Product Reviews." SinkrOn 7, no. 3 (2022): 944–49. http://dx.doi.org/10.33395/sinkron.v7i3.11569.

Full text

Abstract:

The purpose of this study was to compare the accuracy performance of the K-Means and DBScan algorithms in clustering product reviews. This comparison evaluated to determine which algorithm is better in terms of accuracy. The two algorithms were chosen because they have different methods of clustering, K-Means uses centroid-based while DBScan uses density-based. Text clustering results can be implemented on e-commerce platforms, marketplaces or product review platforms. This can help customers in deciding what product they will buy. One of the factors that customers have difficulty in determining what product they will buy is the number of reviews that each product has, and the difficulty in concluding the advantages of each product that will be matched their needs or desires. With text clustering, it can be easier and faster for customer to determine whether the product is worth buying or not based on the product reviews they read. The data set used in this study is a review of the Cetaphil Facial Wash product from the Female Daily website. Firstly, data set goes through the Text Pre-Processing stage; then it will be clustered using two algorithms, K-Means and DBScan. After that, the results of the clustering of the two algorithms calculated for their accuracy performance and the performance results obtained. From the results of this study, it concluded that, in the review clustering of Cetaphil Facial Wash products, DBScan has 99.80% accuracy, which higher to compare with K-Means with only has 99.50% accuracy.

APA, Harvard, Vancouver, ISO, and other styles

Li, Chongyuan, and Mengmeng Yang. "Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm." Journal of Mathematics 2022 (February 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/3661477.

Full text

Abstract:

In order to overcome the problems of poor clustering effect and large error of text knowledge acquisition in traditional text knowledge acquisition methods, a new text knowledge acquisition method of collaborative product design based on genetic algorithm is proposed in this paper. The definition of collaborative product design text knowledge clustering is given. According to the operation process of the genetic algorithm, the chromosomes of clustered text are constructed and encoded and the initial population is obtained. The fitness function of clustering is constructed by the DB index evaluation method; the selection, crossover, and mutation operators in the genetic algorithm are determined; and the objective function of collaborative product design text knowledge clustering is constructed. After the text knowledge clustering is completed, the text knowledge data of collaborative product design are obtained in an all-around way by using the method of rough set and neural network. The experimental results show that compared with the traditional text knowledge acquisition methods, the clustering effect of the proposed method is better and the text knowledge error is reduced up to 0.02.

APA, Harvard, Vancouver, ISO, and other styles

Hung, Che-Yu, and Chien-Chih Wang. "An Approach for Multi-Item Product Sales Forecasting Based on Advancing the BCG Matrix with Matrix-Clustering and Time Modeling Techniques." Systems 12, no. 10 (2024): 388. http://dx.doi.org/10.3390/systems12100388.

Full text

Abstract:

Customized production has greatly diversified product categories, which has altered product life cycles and added complexity to business management. This paper introduces a matrix-clustering technique that integrates k-means clustering with the BCG Matrix, enhanced by time modeling, to offer a comprehensive framework for multi-item product sales forecasting. The approach builds upon existing BCG Matrix outcomes, re-clustering high-selling products more precisely and redefining their relationship with other product lines more objectively. This method addresses the challenge of forecasting situations with limited historical data, providing more accurate sales predictions. Using Taiwan’s sales data, an empirical study on integrated circuit tray products demonstrated the effectiveness of the matrix clustering technique. The results showed improved data utilization, increasing from 35.93% with the original BCG analysis to 52.43% with the combined matrix-clustering and time modeling methods. This study contributes to academic research by presenting a portfolio analysis approach rooted in matrix clustering, which systematically enhances traditional BCG Matrix methods. The proposed framework is adaptable to the unique traits of different portfolios, offering businesses workflows that are efficient, reliable, sustainable, and scalable.

APA, Harvard, Vancouver, ISO, and other styles

Benaissa, Brahim, Masakazu Kobayashi, and Hiroshi Takenouchi. "Enhancing Consumer Agent Modeling Through Openness-Based Consumer Traits and Inverse Clustering." Machine Learning and Knowledge Extraction 7, no. 1 (2025): 9. https://doi.org/10.3390/make7010009.

Full text

Abstract:

This study investigates the relationship between consumer personality traits, specifically openness, and responses to product designs. Consumers are categorized based on their levels of openness, and their affective responses to nine vase designs, varying in curvature and line quantity, are evaluated. The study then introduces the inverse clustering approach, which prioritizes maximizing predictive model accuracy over within-cluster similarity. This method iteratively refines cluster assignments to optimize prediction performance, minimizing errors in forecasting consumer design preferences. The results demonstrate that the inverse clustering approach yields more effective clusters than personality-based clustering. Moreover, while there is some overlap between personality-based and accuracy-based clustering, the inverse clustering method captures additional individual characteristics, extending beyond personality traits and improving the understanding of consumer product design response. The practical implications of this study are significant for product designers, as it enables the development of more personalized designs and optimization of product features to enhance specific consumer perceptions, such as robustness or esthetic appeal.

APA, Harvard, Vancouver, ISO, and other styles

Kajal, Singh. "Product Review Classification using Machine Learning and Statistical Data Analysis." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 2 (2023): 91–96. https://doi.org/10.35940/ijrte.A7530.0712223.

Full text

Abstract:

<strong>Abstract: </strong>The aim of the paper is to implement and analyze the machine learning models for product review dataset. The project focuses on binary classification, multi-class classification, and clustering approaches to analyze and categorize product reviews. The performance of the models over each of the five classification tasks is measured by the 5-fold cross-validation scores over the training data.

APA, Harvard, Vancouver, ISO, and other styles

Chen, Xing Yu, Guo Bin Wu, Han Zhao, and Qi Feng Sun. "Research on Function Module Clustering Based on the Rule-Immunity Algorithm for Complex Product." Applied Mechanics and Materials 742 (March 2015): 364–71. http://dx.doi.org/10.4028/www.scientific.net/amm.742.364.

Full text

Abstract:

A two-step strategy was proposed to solve the problems that inefficiency and inaccuracy of function modules clustering for complex product. Firstly, the three principles were proposed that weldment simplification, outsourcing simplification and borrowed component reduction to preprocess and simplify complex product. Then the complex product preprocessed can be clustered into different function modules by using the advanced Immune Algorithm amalgamated with heuristic rule (R-Immunity). By comparing the efficiency, accuracy and robustness of function modules clustering among the Genetic Algorithm, the Immune Algorithm and the R-Immunity Algorithm, we consider that the R-Immunity Algorithm is more efficient and precise to solve the problems related to function modules clustering. Finally, starting with the structure properties of complex product, the clustering results were optimized for the purposes of reducing coupling between modules and satisfying configuration requirements of customer.

APA, Harvard, Vancouver, ISO, and other styles

Tanioka, Kensuke, and Satoru Hiwa. "Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product." Applied Sciences 11, no. 10 (2021): 4582. http://dx.doi.org/10.3390/app11104582.

Full text

Abstract:

In the domain of functional magnetic resonance imaging (fMRI) data analysis, given two correlation matrices between regions of interest (ROIs) for the same subject, it is important to reveal relatively large differences to ensure accurate interpretation. However, clustering results based only on differences tend to be unsatisfactory and interpreting the features tends to be difficult because the differences likely suffer from noise. Therefore, to overcome these problems, we propose a new approach for dimensional reduction clustering. Methods: Our proposed dimensional reduction clustering approach consists of low-rank approximation and a clustering algorithm. The low-rank matrix, which reflects the difference, is estimated from the inner product of the difference matrix, not only from the difference. In addition, the low-rank matrix is calculated based on the majorize–minimization (MM) algorithm such that the difference is bounded within the range −1 to 1. For the clustering process, ordinal k-means is applied to the estimated low-rank matrix, which emphasizes the clustering structure. Results: Numerical simulations show that, compared with other approaches that are based only on differences, the proposed method provides superior performance in recovering the true clustering structure. Moreover, as demonstrated through a real-data example of brain activity measured via fMRI during the performance of a working memory task, the proposed method can visually provide interpretable community structures consisting of well-known brain functional networks, which can be associated with the human working memory system. Conclusions: The proposed dimensional reduction clustering approach is a very useful tool for revealing and interpreting the differences between correlation matrices, even when the true differences tend to be relatively small.

APA, Harvard, Vancouver, ISO, and other styles

Zhao, Shuangyao, Qiang Zhang, Zhanglin Peng, and Xiaonong Lu. "Product platform configuration for product families: Module clustering based on product architecture and manufacturing process." Advanced Engineering Informatics 52 (April 2022): 101622. http://dx.doi.org/10.1016/j.aei.2022.101622.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Jeon, Ik-Jin, and Hyeon-Seung Lee. "Clustering-based Product Recommendation Model Reflecting Customer Response History and Product Information." Journal of the Korean Operations Research and Management Science Society 48, no. 3 (2023): 29–39. http://dx.doi.org/10.7737/jkorms.2023.48.3.029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Dai, Wei Hui. "Customer Oriented Product Design by Adaptive Clustering Analysis." Applied Mechanics and Materials 26-28 (June 2010): 690–93. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.690.

Full text

Abstract:

In the face of increasing competition and diversified demand, product design must be customer oriented for modern enterprises to satisfy the variationally personalized requirements. Thereof; the exact orientation and comprehensive analysis of target customers have been the key to conduct customer oriented product design. This paper presented a new method for the orientation and analysis of target customers by adaptive clustering analysis. By this method, the distribution of customer clusters and the related characteristics of each custer as well as its included customers can be extracted dynamically and adaptively. Applied by this method, the adaptive recommendation system was illustrated for the design of LCD-TV.This paper applied the Autonomous Intelligent System(AIS) to deal with the intelligent activities intelligently and automatically, and thereof provided a new method for the Supply Chain Management(SCM) on MC manufacture. First, a new supply chain model based on E-HUB was presented according to the requirements of MC manufacture, and then the structure and operation of AIS were designed to support that SCM. Finally, the development technology of AIS was discussed.

APA, Harvard, Vancouver, ISO, and other styles

Chang, Jae-Young. "An Opinion Document Clustering Technique for Product Characterization." Journal of Society for e-Business Studies 19, no. 2 (2014): 95–108. http://dx.doi.org/10.7838/jsebs.2014.19.2.095.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Daie, Pooya, and Simon Li. "Matrix-based hierarchical clustering for developing product architecture." Concurrent Engineering 24, no. 2 (2016): 139–52. http://dx.doi.org/10.1177/1063293x16635721.

Full text

APA, Harvard, Vancouver, ISO, and other styles

QI, Yunhui. "Product structure element clustering design for energy optimization." Chinese Journal of Mechanical Engineering 44, no. 01 (2008): 161. http://dx.doi.org/10.3901/jme.2008.01.161.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Calantone, Roger J., and C. Anthony Di Benedetto. "Clustering product launches by price and launch strategy." Journal of Business & Industrial Marketing 22, no. 1 (2007): 4–19. http://dx.doi.org/10.1108/08858620710722789.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Ye, Xiaoli, and John K. Gershenson. "Attribute-based clustering methodology for product family design." Journal of Engineering Design 19, no. 6 (2008): 571–86. http://dx.doi.org/10.1080/09544820802471123.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Lacko, Daniël, Toon Huysmans, Jochen Vleugels, et al. "Product sizing with 3D anthropometry andk-medoids clustering." Computer-Aided Design 91 (October 2017): 60–74. http://dx.doi.org/10.1016/j.cad.2017.06.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Yin, Ming, Junbin Gao, Shengli Xie, and Yi Guo. "Multiview Subspace Clustering via Tensorial t-Product Representation." IEEE Transactions on Neural Networks and Learning Systems 30, no. 3 (2019): 851–64. http://dx.doi.org/10.1109/tnnls.2018.2851444.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Lorenc, Augustyn, Małgorzata Kuźnar, and Tone Lerher. "Solving product allocation problem (PAP) by using ANN and clustering." FME Transactions 49, no. 1 (2021): 206–13. http://dx.doi.org/10.5937/fme2101206l.

Full text

Abstract:

Proper planning of a warehouse layout and the product allocation in it, constitute major challenges for companies. In the paper, the new approach for the classification of the problem is presented. Authors used real picking data from the Warehouse Management System (WMS) from peak season from September to January. Artificial Neural Network (ANN) and automatic clustering by using Calinski-Harabasz criterion were used to develop a new classification approach. Based on the picking list the clients' orders were prepared and analyzed. These orders were used as input data to ANN and clustering. In this paper, three variants were analyzed: the reference representing the current state, variant with product relocation by using ANN, and the variant with relocation by using automatic clustering. In the research over 380000 picks for almost 1600 locations were used. In the paper, the architecture of the system module for solving the PAP problem is presented. Presented research proved that using multi-criterion clustering can increase the efficiency of the order picking process.

APA, Harvard, Vancouver, ISO, and other styles

Nalavade, Jagannath E., Chandra Sekhar Kolli, and Sanjay Nakharu Prasad Kumar. "Deep embedded clustering with matrix factorization based user rating prediction for collaborative recommendation." Multiagent and Grid Systems 19, no. 2 (2023): 169–85. http://dx.doi.org/10.3233/mgs-230039.

Full text

Abstract:

Conventional recommendation techniques utilize various methods to compute the similarity among products and customers in order to identify the customer preferences. However, such conventional similarity computation techniques may produce incomplete information influenced by similarity measures in customers’ preferences, which leads to poor accuracy on recommendation. Hence, this paper introduced the novel and effective recommendation technique, namely Deep Embedded Clustering with matrix factorization (DEC with matrix factorization) for the collaborative recommendation. This approach creates the agglomerative matrix for the recommendation using the review data. The customer series matrix, customer series binary matrix, product series matrix, and product series binary matrix make up the agglomerative matrix. The product grouping is carried out to group the similar products using DEC for retrieving the optimal product. Moreover, the bi-level matching generates the best group customer sequence in which the relevant customers are retrieved using tversky index and angular distance. Also, the final product suggestion is made using matrix factorization, with the goal of recommending to clients the product with the highest rating. Also, according to the experimental results, the developed DEC with the matrix factorization approach produced better results with respect to f-measure values of 0.902, precision values of 0.896, and recall values of 0.908, respectively.

APA, Harvard, Vancouver, ISO, and other styles

Harahap, Ahir Yugo Nugroho, Ria Eka Sari, Heri Gunawan, and Adnan Buyung Nasution. "Evaluation of Product Sales Data Using Clustering Method and Hierarchical Divisive Clustering at PT.AYN." Indonesian Journal of Interdisciplinary Research in Science and Technology 2, no. 7 (2024): 1145–58. http://dx.doi.org/10.55927/marcopolo.v2i7.10442.

Full text

Abstract:

Data Mining, focusing on the Hierarchical Divisive algorithm, can provide a solution for PT.AYN in overcoming the problem of unifying and evaluating sales data, a company that sells various types of disposable tissues. This study aims to identify products that are in demand and less in demand and to group sales data based on company and product type. The results of this study provide valuable insights for evaluating sales data, understanding distributor purchasing trends, and supporting more effective stock planning, shipping, and marketing strategies. Through the application of the clustering method and the Hierarchical Divisive algorithm, this study offers an effective solution to optimize the use of sales data at PT.AYN, which has the potential to be a valuable asset in formulating long-term business strategies. The conclusion of this study is that the Clustering method and the Hierarchical Divisive algorithm can be used to solve problems in grouping sales data based on various factors, including region and product type, and can assist in developing better marketing strategies.

APA, Harvard, Vancouver, ISO, and other styles

Alganiu, Ajeng Shalwa, Ayu Ratna Juwita, Rahmat Rahmat, and Sutan Faisal. "Perbandingan Algoritma K-Means dan K-Medoids untuk Clustering Pada Transaksi Penjualan Minimarket." Journal of Computer System and Informatics (JoSYC) 6, no. 1 (2024): 14–24. https://doi.org/10.47065/josyc.v6i1.5873.

Full text

Abstract:

When shopping, buyers often have difficulty finding daily necessities. One of the causes of this is because the product arrangement process in minimarkets is still carried out randomly and does not match consumer shopping patterns. On the contrary, buyers usually want to buy products through daily necessities packages, but these packages are usually not yet available in minimarkets. Identifying relationship patterns in minimarket transaction data can help overcome product arrangement and product packaging problems. By using the clustering method, objects are grouped into groups that have many similarities with each other. This method allows the grouping process to be carried out. Some of the methods in clustering include the K-Means and K-medoids methods. The purpose of this study is to group the data on goods in the minimarket which can be a guide for more neatly arranged product planning. Data grouping is divided into 3 categories namely slow, medium and fast. The results obtained show that the two algorithms produce different Davies-Bouldin Index values, with the K Medoids algorithm obtaining a lower value of 0.50387 while K-Means obtains a value of 0.50391 where the K-Medoids clustering results have better quality compared to K-Means. With the results of the grouping of these goods data, minimarkets can balance the stock of goods to prevent excess or shortage of inventory of these goods.

APA, Harvard, Vancouver, ISO, and other styles

Wang, Pu. "A Collaborative Filtering Recommendation Algorithm Based on Product Clustering." Applied Mechanics and Materials 267 (December 2012): 87–90. http://dx.doi.org/10.4028/www.scientific.net/amm.267.87.

Full text

Abstract:

E-commerce recommendation system is one of the most important and the most successful application field of information intelligent technology. Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the customers. Recommendation algorithm is the core of the recommendation system. Collaborative filtering recommendation algorithm is the personalized recommendation algorithm that is used widely in e-commerce recommendation system. Collaborative filtering has been a comprehensive approach in recommendation system. But data are always sparse. This becomes the bottleneck of collaborative filtering. Collaborative filtering is regarded as one of the most successful recommender systems within the last decade, which predicts unknown ratings by analyzing the known ratings. In this paper, an electronic commerce collaborative filtering recommendation algorithm based on product clustering is given. In this approach, the clustering of product is used to search the recommendation neighbors in the clustering centers.

APA, Harvard, Vancouver, ISO, and other styles

Sellgren, U., and D. Williamsson. "ARCHITECTING COMPLEX ENGINEERED SYSTEMS." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 2415–24. http://dx.doi.org/10.1017/dsd.2020.335.

Full text

Abstract:

AbstractNovel products are commonly realized by integrating heterogeneous technologies. Product architecting focus on defining the scheme by which the product functions are allocated to physical components. A DSM-based clustering method that integrates technical complexity and strategic concerns has previously been proposed. It has been shown that interaction weights in the DSM may affect the clustering result. A complexity-based interaction strength model to be used in DSM clustering is proposed here. The case study gives promising results from both interaction performance and safety points of view.

APA, Harvard, Vancouver, ISO, and other styles

Mathivanan, Norsyela Muhammad Noor, Nor Azura Md. Ghani, and Roziah Mohd Janor. "Improving Classification Accuracy Using Clustering Technique." Bulletin of Electrical Engineering and Informatics 7, no. 3 (2018): 465–70. https://doi.org/10.11591/eei.v7i3.1272.

Full text

Abstract:

Product classification is the key issue in e-commerce domains. Many products are released to the market rapidly and to select the correct category in taxonomy for each product has become a challenging task. The application of classification model is useful to precisely classify the products. The study proposed a method to apply clustering prior to classification. This study has used a large-scale real-world data set to identify the efficiency of clustering technique to improve the classification model. The conventional text classification procedures are used in the study such as preprocessing, feature extraction and feature selection before applying the clustering technique. Results show that clustering technique improves the accuracy of the classification model. The best classification model for all three approaches which are classification model only, classification with hierarchical clustering and classification with K-means clustering is K-Nearest Neighbor (KNN) model. Even though the accuracy of the KNN models are the same across different approaches but the KNN model with K-means clustering had the shortest time of execution. Hence, applying K-means clustering prior to KNN model helps in reducing the computation time.

APA, Harvard, Vancouver, ISO, and other styles

Saptadi, Singgih, Ary Arvianto, Wiwik Budiawan Dhima, and Wachid Nur Saputra. "PRODUCT CLUSTERING IN THE MSME BUSINESS OF GROCERY STORE." International Journal of Applied Science and Engineering Review 03, no. 06 (2022): 43–65. http://dx.doi.org/10.52267/ijaser.2022.3604.

Full text

Abstract:

The business world is an exciting world to follow due to its dynamic and competitiveness. Sri Wahyuni Grocery is one of the MSMEs involved in buying and selling daily-basis needs. The business can manage an average of 115 transactions in a day, including various transactions for necessities. Most products purchased at this grocery shop are daily basic needs, such as sugar, tea, instant food, snacks, and fuel oil. It is known that from observations, some products are less desirable by buyers so they are not selling well and some products are selling well, so it is necessary to do a product grouping process to find out how to group these products. An analysis of existing data is needed to obtain information with the K-Means Clustering algorithm. This research aims to determine the pattern of transaction data owned by Traditional Grocery Store MSMEs and form a Clustering pattern of products offered by Traditional Grocery Store MSMEs. Through the data exploration process, this research will carry out a pattern search from transaction data and clustering patterns owned by Traditional Grocery Store MSMEs. Based on the findings from the research conducted, business actors will be able to develop various strategies to improve services to sales by relying on the use of various data mining algorithms. The research was conducted on Traditional Grocery Store MSMEs with transaction data for two months, and the research carried out data exploration to determine clustering patterns using the CRISP-DM method.

APA, Harvard, Vancouver, ISO, and other styles

Zhou, Xiaofeng, and Ying Zhou. "Research on the Innovative Marketing Model of E-Commerce Platform Driven by Big Data in the Era of Network Economy." Mobile Information Systems 2022 (August 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/1551264.

Full text

Abstract:

With the booming development of Internet information technology, e-commerce platforms in the era of network economy have undergone great changes, triggering a new marketing model change. Innovative research on marketing models can help the transformation and development of small and medium-sized e-commerce companies, which has important practical significance and theoretical value. The prediction of e-commerce sales is one of the key aspects of the evaluation of innovative marketing models, and only an accurate prediction of future sales can lead to a reasonable marketing plan. Therefore, a big data-driven e-commerce sales forecasting method is proposed. First of all, for 1703 real e-commerce companies, a large number of relevant data that affect sales are selected, including sales records, product information, product evaluation, and other information. A knowledge graph was then used to preprocess the data samples to produce a sample set containing concepts, entities, and relationships. Next, the knowledge graph K-modes clustering model is established. By fixing the affiliation matrix and the clustering cluster matrix in turn, the minimum of the objective function is continuously solved to obtain the cluster centres. Finally, sales prediction is achieved based on the clustering results. The experimental results show that the proposed clustering model is able to obtain better performance in terms of cluster purity, NMI, and F-value. The proposed clustering model has high sales prediction accuracy and has certain reference value for e-commerce enterprises of different scales to formulate innovative marketing models.

APA, Harvard, Vancouver, ISO, and other styles

Astuti, Juli, and Trisna Yuniarti. "Data Mining Modeling in Clustering Car Products Sales Data in the Automotive Industry in Indonesia." Jurnal Manajemen Industri dan Logistik 7, no. 2 (2023): 261–81. http://dx.doi.org/10.30988/jmil.v7i2.1258.

Full text

Abstract:

The research aims to build a model based on sales data for all automotive products in Indonesia using data mining with a k-means approach. This study uses automotive product sales data from January 2017 to September 2022. The lowest Davis-Bouldin index shows that three clusters (k=3) have the best performance. Based on the clustering results, 92% of the items are in cluster 0, 1% in cluster 1, and 7% in cluster 2. In addition, the clustering results show that cluster 1 is a car product with high sales volume. Cluster 2 is a car product with medium sales volume. Furthermore, cluster 0 is a car product with low sales volume. Business people or related parties can use data visualization and extraction from clustering results to learn the latest insights and information in determining business strategies, policies, and decisions to improve business competitiveness.

APA, Harvard, Vancouver, ISO, and other styles

Widodo, Imam Djati. "Fuzzy subtractive clustering based prediction model for brand association analysis." MATEC Web of Conferences 154 (2018): 01082. http://dx.doi.org/10.1051/matecconf/201815401082.

Full text

Abstract:

The brand is one of the crucial elements that determine the success of a product. Consumers in determining the choice of a product will always consider product attributes (such as features, shape, and color), however consumers are also considering the brand. Brand will guide someone to associate a product with specific attributes and qualities. This study was designed to identify the product attributes and predict brand performance with those attributes. A survey was run to obtain the attributes affecting the brand. Subtractive Fuzzy Clustering was used to classify and predict product brand association based aspects of the product under investigation. The result indicates that the five attributes namely shape, ease, image, quality and price can be used to classify and predict the brand. Training step gives best FSC model with radii (ra) = 0.1. It develops 70 clusters/rules with MSE (Training) is 9.7093e-016. By using 14 data testing, the model can predict brand very well (close to the target) with MSE is 0.6005 and its’ accuracy rate is 71%.

APA, Harvard, Vancouver, ISO, and other styles

Wu, Juebo, and Zongling Wu. "Improved Fuzzy C-Means Clustering for Personalized Product Recommendation." Research Journal of Applied Sciences, Engineering and Technology 6, no. 3 (2013): 393–99. http://dx.doi.org/10.19026/rjaset.6.4092.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Yoo, Jaewook. "Module Communization for Product Platform Design Using Clustering Analysis." Journal of Society of Korea Industrial and Systems Engineering 37, no. 3 (2014): 89–98. http://dx.doi.org/10.11627/jkise.2014.37.3.89.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Dahl, David B. "Modal clustering in a class of product partition models." Bayesian Analysis 4, no. 2 (2009): 243–64. http://dx.doi.org/10.1214/09-ba409.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Yuguang, Zhong, Xue Kai, and Shi Dongyan. "Clustering and group selection of interim product in shipbuilding." Journal of Intelligent Manufacturing 25, no. 6 (2013): 1393–401. http://dx.doi.org/10.1007/s10845-013-0737-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Pham, The-Anh, and Nang-Toan Do. "Embedding hierarchical clustering in product quantization for feature indexing." Multimedia Tools and Applications 78, no. 8 (2018): 9991–10012. http://dx.doi.org/10.1007/s11042-018-6626-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Szilágyi, László. "Robust Spherical Shell Clustering Using Fuzzy-Possibilistic Product Partition." International Journal of Intelligent Systems 28, no. 6 (2013): 524–39. http://dx.doi.org/10.1002/int.21591.

Full text

APA, Harvard, Vancouver, ISO, and other styles

AL-Sharuee, Murtadha Talib, Fei Liu, and Mahardhika Pratama. "Sentiment analysis: dynamic and temporal clustering of product reviews." Applied Intelligence 51, no. 1 (2020): 51–70. http://dx.doi.org/10.1007/s10489-020-01668-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!