A Full Coverage Reconstruction Algorithm for the Electromagnetic Map Based on Affinity Propagation Clustering

Considering the reconstruction of electromagnetic maps without the prior information of electromagnetic propagation environment in the target area, a new algorithm based on affinity propagation clustering is proposed to complete the electromagnetic map reconstruction of the target area from points to surfaces and then from points and surfaces to a larger surface. Firstly, according to the actual situation, the target area is reasonably divided into grids. Electromagnetic data is sampled by distributed sensing nodes, and a certain number of sample points are selected for affinity propagation clustering to determine the locations of centers of sample points. Secondly, for the incomplete sample data, the Kriging algorithm is used to reconstruct the small circular electromagnetic maps. The class center is the center of the circle and the radius is certain. After that, the obtained small area electromagnetic map data and the data obtained from the sample points are used for domain mapping processing, and the electromagnetic data of a larger area of the target area is obtained. Finally, the overall electromagnetic map is reconstructed through data fusion. The simulation results show that the proposed algorithm is better than several interpolation algorithms. When sample points account for 0.1 of total data points, the RMSE of the result is less than 1.5.

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

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.

Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.

Exploring Patterns of Evolution for Successful Global Brands: A Data-Mining Approach

The sustainable development of a global brand needs to consider the balance between the economy, the environment, and society. Brands that want to be ranked among the best global brands over time need to have competitive strengths, but what defines a successful global brand’s profile is underexplored in the extant literature. This study adopts a data-mining approach to analyze the time-series data collected from Interbrand’s Best Global Brands ranking lists. A total of 168 global brands from 19 countries across 24 industries between 2001 and 2017 were examined. Using the affinity propagation clustering algorithm, this study identified certain patterns of brand evolution for different brand clusters, labeled as fast riser, top tier, stable, slow grower, decline, fall, potential, and so on. Finally, the rankings from 2018 to 2020 were also added to check the model’s predictive power. The findings of this study have important marketing implications.