An iterated greedy heuristic for a market segmentation problem with multiple attributes

In order to better apply the K-modes algorithm to intrusion detection, this paper overcomes the problems of the existing K-modes algorithm based on rough set theory. Firstly, for the problem of K-modes clustering in the initial class center selection, an initial class center selection algorithm Ini_Weight based on weighted density and weighted overlap distance is proposed. Secondly, based on the Ini_Weight algorithm, a new K-modes clustering algorithm WODKM based on weighted overlap distance is proposed. Thirdly, the WODKM clustering algorithm is applied to intrusion detection to obtain a new unsupervised intrusion detection model. The model detects the intrusion by dividing the clusters in the clustering result into normal clusters and abnormal clusters and analyzing the weighted average density of the object x to be detected in each cluster and the weighted overlapping distance of x and each center point. We verified the intrusion detection performance of the model on the KDD Cup 99 dataset. The experimental results of the current study show that the proposed intrusion detection model achieves efficient results and solves the problems existing in the present-day intrusion detection system to some extent.

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“…Obviously, the traditional K-modes clustering algorithm has the following problems [17][18][19][20]:…”

Section: K-modes Clustering Algorithmmentioning

confidence: 99%

K-Modes Clustering Algorithm Based on Weighted Overlap Distance and Its Application in Intrusion Detection

Dai

Yuan

Yang

et al. 2021

Scientific Programming

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“…Next, you need to cluster the factor variables to complete customer segmentation. The commonly used clustering algorithm is the K-means algorithm, which randomly selects a set of initial clustering centers and continuously updates iteratively until the clustering results no longer change [14]. However, the determination of the K value in the K-means algorithm is difficult to estimate.…”

Section: Factor Analysis Stepsmentioning

confidence: 99%

Improved Multi-index Customer Segmentation Model Research

Bellotti¹,

Davies²,

Wang³

2021

IJSBT

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Customer segmentation helps the company's strategy formulation and competitiveness enhancement. To better meet customer needs and preferences, companies must recognize the differences of customers and formulate precise marketing strategies. This article focuses on the current customer segmentation background and combines Data mining tools, proposed a multiindex customer segmentation model. Considering the micro and macro perspectives, the traditional indicators are refined, and new segmentation indicators are added. The indicators are weighted by the entropy method. To reduce the time complexity of clustering, factor analysis is used to reduce the data dimension. Finally, the improved K-means clustering algorithm is used to optimize the determination of the K value and the selection of the initial center point to determine the customer segmentation results. The empirical research results on the segmentation of a retailer's membership data show that the improved algorithm is superior to the classic customer segmentation method in terms of clustering compactness and feature division capabilities. With this, it can help companies to improve the level of customer relationship management and the quality of decision-making.

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“…The development of market segmentation theory largely depends on the acquisition of marketing data, the advances in analytical techniques and the progress of segmentation methodology (Wedel and Kamakura, 2012). We refer readers to two recent papers (Huerta-Muñoz et al , 2017; Liu et al , 2019) that provide detailed reviews for quantitative study on various market segmentation approaches and solution methods for different scenarios. Besides the B2C markets, the segmentation is also challenging in B2B markets (Freytag and Clarke, 2001).…”

Section: Literature Review and Theoretical Backgroundmentioning

confidence: 99%

Co-production strategy, retail competition, and market segmentation

Wang

Fan

2019

APJML

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Purpose The purpose of this paper is to examine the effect of manufacturers’ co-production strategy on market segmentation and channel performance under retail competition. Design/methodology/approach It differs from previous empirical studies by primarily focusing on the increment in consumer value accompanying co-production. The authors establish a game-theoretical model to analyze the impact of co-production on market segmentation and the profitability of channel members in a competitive retail environment. Findings The results reveal that manufacturers introducing co-production expand market coverage and benefit all channel members, when the intensity of competition is sufficiently high, especially for retailers with low-quality levels, who are out of the market without co-production. Furthermore, with the increase in customer valuation through co-production, employing a co-production strategy is always a dominant strategy for manufacturers. Research limitations/implications First, although the authors assume a monopoly manufacturer and two duopoly retailers, adding competition between manufacturers should enrich the model. Multiple products with vertical or horizontal differentiation could also be introduced into the model. Second, the authors use the multiplicative utility function to model the value co-creation effect on consumers; however, different utility functions may yield significantly different results and implications. Third, the authors model a one-shot game in a single product selling period; future studies may employ multi-period games to obtain further insight into co-production strategy. Finally, the model assumes that all consumers are homogenous in the extent of value creation and hassle cost. Future research may find it interesting to consider heterogeneity in these characteristics. Practical implications The business world today already sees the power of leadership in a supply chain to have shifted from manufacturers to retail giants such as Walmart, Home Depot and Best Buy. The findings also propose a new route to counteract the emergence and rise of dominant retailers. On the other hand, with the application of new technology in the retail industry such as 3D avatar, AR/VR, Internet of Things, consumers are more likely to participate in various forms of co-production activities, how to execute the co-production strategy has become more and more important for managers. Social implications The conclusion of this study points out the way to achieve a win–win outcome under which both channel members including manufacturer and retailers and consumers can be better off, that is, the channel can reach Pareto improvement, so the social welfare is increased accordingly. Originality/value The authors propose an analytical framework to examine the effects of co-production and competition on market segmentation and profitability, and prove that co-production is a powerful marketing tool that can attract consumers and increase profitability, which manufacturers can incorporate into their products even in a competitive environment.

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An iterated greedy heuristic for a market segmentation problem with multiple attributes

Cited by 29 publications

References 55 publications

K-Modes Clustering Algorithm Based on Weighted Overlap Distance and Its Application in Intrusion Detection

K-Modes Clustering Algorithm Based on Weighted Overlap Distance and Its Application in Intrusion Detection

Improved Multi-index Customer Segmentation Model Research

Co-production strategy, retail competition, and market segmentation

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