Improvisation of spectral clustering through affinity propagation

Kalpana, P.; Nivetha, A. M.; Madhumitha, R.; Tamijeselvy, P.; Devi, Sobha Lalitha

doi:10.1063/5.0073478

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…AP sendiri merupakan algoritma yang cenderung lebih stabil karena algoritma ini mencari solusi yang mengoptimalkan keseluruhan data [15], [16] dan tidak bergantung pada inisialisasi centroid awal seperti pada K Means. DBSCAN dan AP sama-sama algoritma yang tidak memerlukan inisiasi awal jumlah segmen.…”

Section: Pendahuluanunclassified

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Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation

Agustino¹,

Budaya²,

Harsemadi³

et al. 2023

SISFOKOM

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The increasingly complex business environment necessitates businesses to design more effective and efficient strategies for company development, including market expansion. To understand customer behaviors, customer data analysis becomes crucial. One common approach used to group customers is segmentation based on RFM analysis (Recency, Frequency, and Monetary). This study aims to compare the performance of two clustering algorithms, namely DBSCAN and Affinity Propagation (AP), in providing customer profile segment recommendations using RFM analysis. DBSCAN algorithm is employed due to its ability to identify arbitrarily shaped clusters and handle data noise. On the other hand, Affinity Propagation (AP) algorithm is chosen for its capability to discover cluster centers without requiring a pre-defined number of clusters. The transaction dataset used in this research is obtained from one of the business incubator tenants at STIKOM Bali. The dataset undergoes preprocessing steps before being segmented using both DBSCAN and AP algorithms. Performance evaluation of the algorithms is conducted using the Silhouette Scores and Davies-Bouldin Index (DBI) matrices. The research findings indicate that the AP algorithm outperforms DBSCAN in this customer segmentation case. The AP algorithm yields Silhouette Scores of 0.699 and DBI of 0.429, along with recommendations for 4 customer segments. Furthermore, further analysis is performed on the AP results using a statistical approach based on the mean values of each segment for the RFM variables. The four customer segments generated by the AP algorithm, based on the mean values of the RFM variables, can be associated with the concept of customer relationship management.

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Section: Pendahuluanunclassified

“…Algoritma AP Algoritma AP sendiri merupakan algoritma yang cenderung lebih stabil karena algoritma ini mencari solusi yang mengoptimalkan keseluruhan data [15], [16] dan tidak bergantung pada inisialisasi centroid awal seperti pada K Means.…”

Section: )unclassified

Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation

Agustino¹,

Budaya²,

Harsemadi³

et al. 2023

SISFOKOM

View full text Add to dashboard Cite

show abstract

Improvisation of spectral clustering through affinity propagation

Cited by 1 publication

References 13 publications

Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation

Comparison of the DBSCAN Algorithm and Affinity Propagation on Business Incubator Tenant Customer Segmentation

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