A density invariant approach to clustering

Kashyap, Manish; Bhattacharya, Mahua

doi:10.1007/s00521-015-2145-z

Cited by 6 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, as respect to the segmentation methods, the clustering algorithms, as a kind of current mainstream market segmentation method, have been widely applied in tourism, transportation behavior change and other fields to implement an effect market segmentation (Knuth et al, 2019;Arian et al, 2021;Mauricio et al, 2021); they can be broadly divided into two categories: partitioning and hierarchical clustering algorithms (Kashyap and Bhattacharya, 2017). In terms of the partition clustering, the K-means algorithm (KMA) is the most popular and frequently used market segmentation algorithm (Arunachalam and Kumar, 2018;France and Ghose, 2019), which first conducts a comprehensive investigation about consumer information from the perspective of the consumers' geographical, demographic, psychological, behavioral and other characteristics, then clusters these consumers according to the similarity between consumers, and finally obtains differentiated segments (Casas-Rosal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

User value identification based on an improved consumer value segmentation algorithm

Jin

et al. 2022

View full text Add to dashboard Cite

PurposeThe purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.Design/methodology/approachIn this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.FindingsThe results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.Practical implicationsThis paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.Originality/valueThis study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

show abstract

Section: Introductionmentioning

confidence: 99%

User value identification based on an improved consumer value segmentation algorithm

Jin

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Density-based clustering has been widely adopted in the literature [1,2,3]. Moreover, it recently attracted an increasing amount of attention in data mining and pattern recognition [4,5].…”

Section: Introductionmentioning

confidence: 99%

McDPC: multi-center density peak clustering

Wang

Zhang

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC's hypothesis, one and only one data point is chosen as the center of any cluster. However, DPC may fail to identify clusters with multiple density peaks (multi-centers) and may not be able to identify natural clusters whose centers have relatively lower local density. To address these limitations, we propose a novel clustering algorithm based on a hierarchical approach, named Multi-center Density Peak Clustering (McDPC). Firstly, based on a widely adopted hypothesis that the potential cluster centers are relatively far away from each other. McDPC obtains centers of the initial micro-clusters (named representative data points) whose minimum distance to the other higher-density data points are relatively larger. Secondly, the representative data points are autonomously categorized into different density levels. Finally, McDPC deals with micro-clusters at each level and if necessary, merges the micro-clusters at a specific level into one cluster to identify multi-center clusters. To evaluate the effectiveness of our proposed McDPC algorithm, we conduct experiments on both synthetic and realworld datasets and benchmark the performance of McDPC against other state-ofthe-art clustering algorithms. We also apply McDPC to perform image segmentation and facial recognition to further demonstrate its capability in dealing with real-world applications. The experimental results show that our method achieves promising performance.

show abstract

LD-IDH-Clu: A New Clustering Algorithm Based on the Local Density Estimation and an Improved Density Hierarchy Strategy

Jin

et al. 2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

A density invariant approach to clustering

Cited by 6 publications

References 28 publications

User value identification based on an improved consumer value segmentation algorithm

User value identification based on an improved consumer value segmentation algorithm

McDPC: multi-center density peak clustering

LD-IDH-Clu: A New Clustering Algorithm Based on the Local Density Estimation and an Improved Density Hierarchy Strategy

Contact Info

Product

Resources

About