K-means Clustering Using R A Case Study of Market Segmentation

Hung, Phan Duy; Ngọc, Nguyen Thi Hong; Hanh, Tran Duc

doi:10.1145/3317614.3317626

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…This study can be applied to most e-commerce sites because it solves the basic problems of online shopping such as Cold-start and sparsity [19,20]. Multi-behaviors property enables it to be applied in the Movie recommendation domain.…”

Section: Discussionmentioning

confidence: 99%

Heterogeneous Neural Collaborative Filtering for a Business Recommender System

Quan

Hung

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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In recent years, lots of techniques applied and optimized to make the recommender system better. Most of them mainly focus on the target interaction between users and items or a part of auxiliary information but hardly leverage other useful information relevant to customer transactions. In this paper, we want to propose a new model named Heterogeneous Neural Collaborative Filtering (HNCF) for learning recommender systems from two important parts: multi-aspects and multi-behaviors. The HNCF algorithm proposed is divided into four parts: Commuting similarity matrix, Multi-Layer Perceptron, Fusion by the Attention Mechanism, Multi-behavior Prediction. The model exploits characteristics of customers and properties of products from different aspects besides the aspect of purchase by building meta paths then commuting similarity matrices. Aspect-level latent factors fusion gives results as factors representing each user and item. These factors synthesizing with multi-behaviors prediction layers is the highlight of the model to make a better recommender system.

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Section: Discussionmentioning

confidence: 99%

Heterogeneous Neural Collaborative Filtering for a Business Recommender System

Quan

Hung

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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“…The result of customer clustering can be used in marketing strategies, forecasting of affordability, or customer deposits in the following month, etc. The paper is also a valuable reference for problems in areas such as Knowledge Representation [28], [29], Prediction [30], Time Series Analysis [31], etc. It can also be applied when dealing with time series data such as biomedical signals, insurance expenditures, etc.…”

Section: Discussionmentioning

confidence: 99%

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

Hung¹,

Dat²

2020

IJMLC

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Customer clustering, the division of customers into different groups, is a classical problem. It is especially important in banking as it serves multiple purposes in marketing, risk management, etc. Therefore, it has attracted the use of many modern machine learning models and techniques. But currently, most of them are only making use of "static" customer information. This paper proposes a new approach for customer clustering in banking based on the customers' balance history. Basic Dynamic Time Warping (DTW) distance and Soft-DTW (SDTW) distance, an advanced form, are used to measure the difference between customers. To which, the two most popular strategies in time series clustering strategies, partitional and hierarchical, are applied which. In additional, some statistical features are given to prove the effectiveness of the proposed method.

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“…Preprocessing is performed at the data exploration stage. This stage includes data cleaning, data integration, and data transformation [19], [20]. All data obtained from the previous stage will be processed at the exploration stage.…”

Section: Methodsmentioning

confidence: 99%

Hybrid clustering based on multi-criteria segmentation for higher education marketing

Khusnuliawati

Putri

2021

TELKOMNIKA

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Market segmentation in higher education institutions is still rarely applied although it can assist in defining the right strategies and actions for the targeted market. The problem that often arises in market segmentation is how to exploit the preferences of students as customers. To overcome this, the combination of hybrid clustering method with multiple criteria will be applied to the case of the market segmentation for students in higher education institutions. The integration of geographic, demographic, psychographic, and behavioral criteria from students is used to get more insightful information about student preference. Data result of the integration will be processed using hybrid clustering using K-means and self organizing map (SOM) algorithm. The hybrid clustering conducted to get promising clustering result along with the visualization of segmentation. This study successfully produces five student segments. It received 1,386 as the Davies-Bouldin index (DBI) value and 2,752 as the quantization error (QE) value which indicates a good clustering result for market segmentation. In addition, the visualization of the clustering result can be seen in a hexagonal map.

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K-means Clustering Using R A Case Study of Market Segmentation

Cited by 14 publications

References 19 publications

Heterogeneous Neural Collaborative Filtering for a Business Recommender System

Heterogeneous Neural Collaborative Filtering for a Business Recommender System

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

Hybrid clustering based on multi-criteria segmentation for higher education marketing

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