Research on Mini-Batch Affinity Propagation Clustering Algorithm

Xu, Zhihai; Lu, Yao; Jiang, Yu

doi:10.1109/dsaa54385.2022.10032450

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Affinity Propagation memanfaatkan "propagation" antara data poin untuk menentukan pusat kelompok. Dalam algoritma ini, setiap poin berkomunikasi dengan yang lain untuk memutuskan pusat kelompok, menangkap struktur data yang kompleks [6].…”

Section: Metodologiunclassified

Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis

Purba,

Handhayani

2024

KOMPUTA

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Market segmentation is the process of dividing a market into homogeneous groups of buyers based on certain characteristics. Market segmentation is important for businesses to understand the needs and behaviors of their customers so that they can develop more effective marketing strategies.This study compares three clustering methods, namely K-Means Clustering, Affinity Propagation Clustering, and Mini Batch K-Means, in the context of market segmentation analysis. The data used is the marketing_campaign.csv dataset consisting of 29 columns and 2240 rows. Experiments were conducted to evaluate the performance of the three methods using the silhouette metric. The results of the experiments showed that Affinity Propagation produced the highest silhouette value of 0.5861, followed by K-Means with 0.4675, and Mini Batch K-Means with 0.4659. The finding indicates that Affinity Propagation can be a good choice for market segmentation analysis on that dataset. The results of this comparison provide insights into the strengths and weaknesses of each clustering method, helping users choose the approach that is most appropriate for their analytical goals. The study contributes to the practical understanding of the application of clustering techniques in the context of marketing.

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Section: Metodologiunclassified

Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis

Purba,

Handhayani

2024

KOMPUTA

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Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning

Jiang,

Xu,

et al. 2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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ECAP: Ensemble Clustering using Affinity Propagation

Sinha,

Ranjan,

Sonu

et al. 2024

Eng. Technol. Appl. Sci. Res.

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A vast amount of time-series data is generated from multiple fields. Mining these data can uncover hidden patterns and behavior characteristics. The analysis of such data is complex because they are voluminous and have high dimensions. Clustering can provide a preprocessing step to extract insights. However, clustering such data poses challenges, as many existing algorithms are not efficient enough to handle them. In addition, many traditional and modern clustering algorithms need help with parameter-tuning problems. Ensemble clustering, an amalgamation of clustering algorithms, has emerged as a promising method for improving the accuracy, stability, and robustness of clustering solutions. This study presents Ensemble clustering using Affinity Propagation (ECAP). AP is efficient because it does not require the number of clusters to be specified a priori, allowing the data to reveal its structure. When used in an ensemble framework, the inherent strengths of AP are amplified by integrating multiple clustering results. This aggregation mitigates the influence of any single, potentially suboptimal clustering outcome, leading to more stable and reliable clusters. Extensive experiments were performed on four real-world datasets for rand index, homogeneity, completeness, and V-measure to determine the efficacy of the proposed approach. The results show that the proposed method outperforms AP, Kmeans, and spectral clustering.

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Research on Mini-Batch Affinity Propagation Clustering Algorithm

Cited by 3 publications

References 16 publications

Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis

Comparison of K-Means, Affinity Clustering, and Mini Batch K-Means Algorithms for Market Segmentation Analysis

Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning

ECAP: Ensemble Clustering using Affinity Propagation

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