The impact of big data market segmentation using data mining and clustering techniques

Yoseph, Fahed; Malim, Nurul Hashimah Ahamed Hassain; Heikkilä, Markku; Brezulianu, Adrian; Geman, Oana; Rostam, Nur Aqilah Paskhal

doi:10.3233/jifs-179698

Cited by 37 publications

(25 citation statements)

References 28 publications

(73 reference statements)

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“…e first step of data clustering is data preprocessing and feature extraction. e process description of data clustering [5,6] is shown in Figure 1. e main steps of this method are as follows:…”

Section: Artistic Modeling Of Opera Stage Clothing Based On Big Data Clustering Algorithmmentioning

confidence: 99%

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Luo

2021

Security and Communication Networks

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In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm. The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone programming model to correct the big data. Aiming at the problem that traditional stage costume art analysis methods cannot correct the clustering results of big data. On this basis, the costume elements of the opera stage are segmented, initialized, and transformed into a binary function. Finally, using the convolutional neural network, combining the element segmentation results and the large data clustering space state vector, a feature extraction model of stage costume art is constructed. Experimental results show that the model has good convergence, short time-consuming, high accuracy, and ideal feature recognition capabilities.

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Section: Artistic Modeling Of Opera Stage Clothing Based On Big Data Clustering Algorithmmentioning

confidence: 99%

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Luo

2021

Security and Communication Networks

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“…However, the consumer behavior preference among different customer groups cannot be well identified. Yoseph et al [ 25 ] studied consumer behavior (e.g., clicking on product links, browsing products, and adding to cart) and purchasing power, and added these features to the RFM model so that consumer categories could be accurately identified and differentiated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The higher the frequency is, the more likely consumers are to buy the product. The introduction of these two indicators into the RFM model can effectively improve the effectiveness of the RFM model for e-commerce customer segmentation [ 25 ].…”

Section: Empirical Analysismentioning

confidence: 99%

Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm

Jin

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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In view of the shortcomings of traditional clustering algorithms in feature selection and clustering effect, an improved Recency, Frequency, and Money (RFM) model is introduced, and an improved K-medoids algorithm is proposed. Above model and algorithm are employed to segment customers of e-commerce. First, traditional RFM model is improved by adding two features of customer consumption behavior. Second, in order to overcome the defect of setting K value artificially in traditional K-medoids algorithm, the Calinski–Harabasz (CH) index is introduced to determine the optimal number of clustering. Meanwhile, K-medoids algorithm is optimized by changing the selection of centroids to avoid the influence of noise and isolated points. Finally, empirical research is done using a dataset from an e-commerce platform. The results show that our improved K-medoids algorithm can improve the efficiency and accuracy of e-commerce customer segmentation.

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“…en, based on the demands of target customers, enhance customer value and give customized services to them. Potential customers can be attracted to buy enterprise products promptly in this way, and precision marketing can be accomplished [23]. As a result of using data mining technologies, enterprises' marketing activities are transformed from passive and fixed procedures to measurable and controllable activities.…”

Section: Application Of Data Mining In Enterprise Precisionmentioning

confidence: 99%

Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

Meng

2022

Mobile Information Systems

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Due to the explosive growth of massive consumption data, the traditional marketing model of enterprises has become stretched. The rapid advancement of data mining technology has given new impetus to the innovation of the marketing strategies of enterprises, promoting the progressive transformation of enterprises from traditional passive marketing to precise and refined marketing. Data mining technology’s basic task is to analyze data, acquire insight into cause and effect, and then predict the future. As a result, the technology is coupled with the enterprises’ marketing activities, and a precise marketing model is built using big data consumption. This will aid in the comprehensive and three-dimensional description of customers, as well as the analysis of potential customers’ attributes, to give a scientific basis for the formulation and implementation of precise data-driven decision-making for enterprises. As a result, our study enhances and optimizes the K-means algorithm in combination with the artificial bee colony algorithm aiming at fixing the issue that the K-means algorithm is sensitive to cluster center initialization and improving the enterprise precision marketing model’s clustering performance. In the precise marketing scenario of the telecom business, the improved K-means clustering model is utilized to realize the analysis and prediction of telecom customers, as well as to carry out precise marketing based on the predicted findings. Finally, the optimized K-means clustering algorithm can objectively and comprehensively reflect the characteristics of telecom customer value segmentation, efficiently mining future clients and preventing blind marketing by enterprises, based on the model's actual verification results. Simultaneously, it provides substantial data support for telecom enterprises’ resource planning, as well as pointing out the next step in increasing market share.

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The impact of big data market segmentation using data mining and clustering techniques

Cited by 37 publications

References 28 publications

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm

Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

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