clustering techniques
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2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Luis Lorenzo ◽  
Javier Arroyo

AbstractSince the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.


2022 ◽  
Vol 14 (2) ◽  
pp. 777
Author(s):  
Carlos Alonso de Armiño ◽  
Daniel Urda ◽  
Roberto Alcalde ◽  
Santiago García ◽  
Álvaro Herrero

Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.


2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Sebastian-Camilo Vanegas-Ayala ◽  
Julio Barón-Velandia ◽  
Daniel-David Leal-Lara

Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.


Author(s):  
Meng Yuan ◽  
Justin Zobel ◽  
Pauline Lin

AbstractClustering of the contents of a document corpus is used to create sub-corpora with the intention that they are expected to consist of documents that are related to each other. However, while clustering is used in a variety of ways in document applications such as information retrieval, and a range of methods have been applied to the task, there has been relatively little exploration of how well it works in practice. Indeed, given the high dimensionality of the data it is possible that clustering may not always produce meaningful outcomes. In this paper we use a well-known clustering method to explore a variety of techniques, existing and novel, to measure clustering effectiveness. Results with our new, extrinsic techniques based on relevance judgements or retrieved documents demonstrate that retrieval-based information can be used to assess the quality of clustering, and also show that clustering can succeed to some extent at gathering together similar material. Further, they show that intrinsic clustering techniques that have been shown to be informative in other domains do not work for information retrieval. Whether clustering is sufficiently effective to have a significant impact on practical retrieval is unclear, but as the results show our measurement techniques can effectively distinguish between clustering methods.


Author(s):  
Maxime C. Cohen ◽  
Paul-Emile Gras ◽  
Arthur Pentecoste ◽  
Renyu Zhang

2022 ◽  
pp. 217-234
Author(s):  
Elhoucine Essefi ◽  
Soumaya Hajji

This chapter aimed to investigate the record of climatic and environmental change in the sedimentary filling of sebkha Mhabeul and their effect on hydric and eolian erosion within the wetland and its watershed. Along a 37 cm core, the sedimentary, geochemical, and geophysical signals at the Holocene-Anthropocene transition were followed. Sampling was carried out each 1 cm to obtain 37 samples. All studied parameters and clustering techniques indicate that the first 7 cm represent the Anthropocene strata. According to the age model, this upper part of the core records the last 300 yrs. The sedimentary record of the Anthropocene is marked by an increasing rate of sedimentation, grain size fining, heavy metals (Pb, Cu, Ni, Mn, and Fe) enrichment, which is related to increased erosion. Other intrinsic parameters such as CE, pH, Na, K, and CaCO3 enhance sediment erodibility. The measurement of the magnetic susceptibility along a 37 cm core collected from the sebkha Mhabeul shows an obvious upward increase related to a high content of heavy metals for the first 7 cm.


2022 ◽  
Author(s):  
Fasahat Ullah Siddiqui ◽  
Abid Yahya

2022 ◽  
pp. 116380
Author(s):  
Jorge S.S. Júnior ◽  
João Ruivo Paulo ◽  
Jérôme Mendes ◽  
Daniela Alves ◽  
Luís Mário Ribeiro ◽  
...  

2022 ◽  
Vol 13 (2) ◽  
pp. 165-184 ◽  
Author(s):  
Andrés Felipe León Villalba ◽  
Elsa Cristina González La Rotta

This article presents a novel algorithm based on the cluster first-route second method, which executes a solution through K-means and Optics clustering techniques and Nearest Neighbor and Local Search 2-opt heuristics, for the solution of a vehicle routing problem with time windows (VRPTW). The objective of the problem focuses on reducing distances, supported by the variables of demand, delivery points, capacities, time windows and type of fleet in synergy with the model's taxonomy, based on data referring to deliveries made by a logistics operator in Colombia. As a result, good solutions are generated in minimum time periods after fulfilling the agreed constraints, providing high performance in route generation and solutions for large customer instances. Similarly, the algorithm demonstrates efficiency and competitiveness compared to other methods detailed in the literature, after being benchmarked with the Solomon instance data set, exporting even better results.


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