Evaluation of the Clustering Performance of Affinity Propagation Algorithm Considering the Influence of Preference Parameter and Damping Factor

Moiane, André Fenias; Machado, Álvaro Muriel Lima

doi:10.1590/s1982-21702018000400027

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…The number of clusters can be generated according to what a user specifies by adding one constraint in the message passing process to restrict the number of clusters to K [15]. While the AP is a parameter set by its users, another advantage of this method is the belief in an object to serve as an example which is automatically adapted by k-affinity propagation [10], [18].…”

Section: K-affinity Propagationmentioning

confidence: 99%

MSME Sales Clustering Based on Business Aid Distribution Priority Using K-Affinity Propagation

Tarisya Qurrota A'yuni,

Baiq Nina Febriati,

Lazuardy Ilham Effendie

et al. 2023

ENTHUSIASTIC

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In rural areas of Indonesia, micro, small, and medium enterprises (MSMEs) are often isolated; however, they have been proven to play an important role as the economic backbone of millions of communities. In fact, the sluggish development of MSMEs in Indonesia become a severe problem for the community welfare. The government continues to strive for the welfare of the local communities, one of which is by supporting the existing MSMEs. However, the provision of government assistance may not be optimal for the incorrect target of the MSMEs. This study informs the government and other related parties regarding subdistrict groups whose MSMEs are considered to be their target. The k-affinity propagation method was used to find a set of representative examples, called exemplars, that best summarize the data. The result shows that sub-districts clusters based on general welfare in five commodities. K-affinity propagation algorithm clusters vary by commodity. Data fluctuation from each commodity’s three factors causes this. From this research, it can be determined which subdistricts have the most or least prosperous MSMEs in each of the five commodities analyzed.

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Section: K-affinity Propagationmentioning

confidence: 99%

MSME Sales Clustering Based on Business Aid Distribution Priority Using K-Affinity Propagation

Tarisya Qurrota A'yuni,

Baiq Nina Febriati,

Lazuardy Ilham Effendie

et al. 2023

ENTHUSIASTIC

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“…K-AP produces K clusters based on predetermined needs and parameters in terms of determining rules or controls in the message delivery process. Another advantage of this method is the belief in an object to serve as an exemplar which is automatically adapted by K-AP, while the AP is a parameter set by its users [20]. Besides, the overhead (memory usage during processing) computation of K-AP is insignificant compared to the AP.…”

Section: K-affinity Propagationmentioning

confidence: 99%

K-affinity propagation clustering algorithm for the classification of part-time workers using the internet

Asriny

Muhajir

Andrian

2021

IJEECS

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There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.

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“…Therefore, the preference value must be swept to achieve optimal clustering performance. However, this approach inevitably entails an extremely high computational complexity, as shown in [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network

Park

Mun

et al. 2021

Sensors

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Affinity propagation (AP) clustering with low complexity and high performance is suitable for radio remote head (RRH) clustering for real-time joint transmission in the cloud radio access network. The existing AP algorithms for joint transmission have the limitation of high computational complexities owing to re-sweeping preferences (diagonal components of the similarity matrix) to determine the optimal number of clusters as system parameters such as network topology. To overcome this limitation, we propose a new approach in which preferences are fixed, where the threshold changes in response to the variations in system parameters. In AP clustering, each diagonal value of a final converged matrix is mapped to the position (x,y coordinates) of a corresponding RRH to form two-dimensional image. Furthermore, an environment-adaptive threshold value is determined by adopting Otsu’s method, which uses the gray-scale histogram of the image to make a statistical decision. Additionally, a simple greedy merging algorithm is proposed to resolve the problem of inter-cluster interference owing to the adjacent RRHs selected as exemplars (cluster centers). For a realistic performance assessment, both grid and uniform network topologies are considered, including exterior interference and various transmitting power levels of an RRH. It is demonstrated that with similar normalized execution times, the proposed algorithm provides better spectral and energy efficiencies than those of the existing algorithms.

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Evaluation of the Clustering Performance of Affinity Propagation Algorithm Considering the Influence of Preference Parameter and Damping Factor

Cited by 10 publications

References 15 publications

MSME Sales Clustering Based on Business Aid Distribution Priority Using K-Affinity Propagation

MSME Sales Clustering Based on Business Aid Distribution Priority Using K-Affinity Propagation

K-affinity propagation clustering algorithm for the classification of part-time workers using the internet

RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network

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