An ensemble clustering approach for modeling hidden categorization perspectives for cloud workloads

Daraghmeh, Mustafa; Agarwal, Anjali; Jararweh, Yaser

doi:10.1007/s10586-023-04205-5

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…The evaluation of clustering algorithms is also a burgeoning field of study, with metrics such as the Caliński-Harabasz index, the Davies-Bouldin index, and modularity measures providing insights into the quality of clustering results (Wei et al, 2021;Daraghmeh, Agarwal and Jararweh, 2023;Bihari et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

Use of Autoencoder and One-Hot Encoding for Customer Segmentation

Smutek,

Sikora,

Bogacki

et al. 2024

ERSJ

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Purpose:The research article aims to apply Autoencoder and One-Hot Encoding techniques to the segmentation of retail customers, exploring how these methodologies can contribute to a more refined and actionable segmentation process. Design/Methodology/Approach: The study uses a dataset comprising detailed profiles of 2240 retail customers, applying Autoencoders and One-Hot Encoding to categorize customers into distinct segments. It evaluates Autoencoders' embeddings and compares them with the traditional One-Hot Encoding method. The effectiveness of the segmentation is further analyzed using various clustering algorithms, including K-means, DBSCAN, Louvain Community Detection, Greedy Modularity, and Label Propagation. The research assesses clustering quality using indices such as the Caliński-Harabasz, Davies-Bouldin, and modularity metrics. Findings: Application of the Louvain method with a cut-off parameter of 0.75 using AutoEmbedder revealed three evenly distributed customer groups, albeit with slightly lower Caliński-Harabasz and Davies-Bouldin index values than those obtained by the Greedy method using AutoEmbedder with a cut-off parameter of 0.5. However, the Louvain method exhibited higher modularity, indicating more cohesive segmentation. Comparisons between AutoEmbedder and One-Hot Encoding suggested the superiority of AutoEmbedder in forming customer clusters. Practical Implications: The findings present actionable insights for marketing strategists to develop targeted campaigns based on customer expenditure patterns. By identifying customer segments with similar attributes, businesses can allocate marketing resources more effectively and tailor strategies to meet the specific needs of each segment. Originality/Value: The article introduces a novel comparison between Autoencoder embeddings and traditional One-Hot Encoding in the context of customer segmentation, providing evidence of the former's enhanced capability in creating more meaningful and modular customer groups. It also extends the discussion on clustering quality assessment in the segmentation process, adding value to marketing analytics.

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Section: Introductionmentioning

confidence: 99%

Use of Autoencoder and One-Hot Encoding for Customer Segmentation

Smutek,

Sikora,

Bogacki

et al. 2024

ERSJ

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Applying computational methods to analyze trends and themes in Library and Information Science Education

Cen

2024

Educ Inf Technol

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Advanced methods for target audience identification: enhancing marketing strategies through machine learning and data analytics

Bogacki,

Rymarczyk,

Smutek

et al. 2024

JoMS

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Purpose: This article presents a novel approach that leverages advanced data analytics and machine learning techniques to enhance marketing strategies. By precisely targeting and segmenting audience groups based on their descriptive profiles, the study aims to significantly improve the efficacy of marketing campaigns. Methods: The study employs several clustering and community detection algorithms, including Louvain Community, Greedy Modularity, and Label Propagation. These methods are applied to diverse datasets to identify distinct groups within the audience that exhibit specific behavioral and preference patterns. The approach emphasizes data-driven decision-making, which involves making decisions based on the analysis of data rather than intuition or observation, to optimize marketing outcomes. Results demonstrate that employing advanced clustering techniques can significantly refine the segmentation process, leading to more targeted marketing efforts. These methods successfully identified nuanced sub-groups within the datasets, which corresponded closely with customer behaviors and preferences variations, thereby allowing for more tailored marketing strategies. Discussion: The study's findings underscore the imperative for marketers to embrace sophisticated analytical techniques. Machine learning has the potential to transform marketing strategies by providing deeper insights into customer segmentation. This research highlights the importance of staying ahead of the curve in the face of the complexities of consumer markets and evolving business environments.

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An ensemble clustering approach for modeling hidden categorization perspectives for cloud workloads

Cited by 4 publications

References 44 publications

Use of Autoencoder and One-Hot Encoding for Customer Segmentation

Use of Autoencoder and One-Hot Encoding for Customer Segmentation

Applying computational methods to analyze trends and themes in Library and Information Science Education

Advanced methods for target audience identification: enhancing marketing strategies through machine learning and data analytics

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