Characterization of well logs using K-mean cluster analysis

Ali, Amjad; Chen, Sheng‐Chang

doi:10.1007/s13202-020-00895-4

Cited by 30 publications

(8 citation statements)

References 21 publications

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“…For the numerical dataset, the centre of each cluster is represented by the mean/centroid. In each cluster, every observation belongs to the nearest mean, serving as a prototype of the cluster (Ali & Sheng-Chang, 2020;Singh et al, 2020;Huseynov & Özdenizci Köse, 2022). The K-means algorithm is an iterative process that stops once the cluster means no longer changes much in successive steps (Orakoglu & Ekinci, 2013).…”

Section: Methodsmentioning

confidence: 99%

Cultural Differences in Coping with Changes in the External Environment: A Case of Behavioural Segmentation of Senior Consumers Based on Their Reaction to the COVID-19 Pandemic

Kopaničová¹,

Vokounová²

2023

CEBR

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A sudden change in the external environment, such as the COVID-19 pandemic meant for the senior customers in CEE, required coping mechanisms to adapt and a significant change in behavioural patterns. The paper presents what were the social and customer behaviour changes among elderly citizens caused by the pandemic and aims to identify regional differences in adapting to these changes. Results from CATI interviews of 5875 participants from Germany, the Czech Republic, and Slovakia, which had been conducted as a part of the international SHARE project bring a deeper understanding of regional differences. Behavioural segmentation done in each of the researched countries shows that the created clusters in each country are different and yet similar in certain aspects. Clusters of "adapters" seniors with partial behavioural change are found in all three countries, even though they vary in some aspects of behaviour, they have significant commonalities as well. A cluster of "vulnerable"seniors that had undergone a complete change of behavioural patterns to protect themselves -or a cluster of "resistant"seniors resisting the change are found only in two of researched countries. Differences in understanding the threat and feeling the need to react in certain ways are found not only between countries but also within cultures. By employing Hofstede´s cultural dimensions score to explain detected differences, the article brings more understanding of how Power Distance, Indulgence, and Individualism could form differences in the usage of coping mechanisms among the elderly. The results bring insight into important aspects of changes in behaviour, which should be understood and mirrored in the business strategies of companies whose actual or potential customers are represented by this population. Implications for Central European audience: The research on behavioural changes among senior consumers due to the Covid-19 pandemic had been done in three countries of CEE -Slovakia, the Czech Republic, and Germany and therefore, the implications are well suitable for the CEE region, yet the generalization to other countries must be done carefully as the results show that created behavioural segments in researched countries indicate cultural differences.

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Section: Methodsmentioning

confidence: 99%

Cultural Differences in Coping with Changes in the External Environment: A Case of Behavioural Segmentation of Senior Consumers Based on Their Reaction to the COVID-19 Pandemic

Kopaničová¹,

Vokounová²

2023

CEBR

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“…We performed it using the k-means method as an algorithm that groups similar objects into groups called clusters. The result of cluster analysis is a set of clusters, where each cluster is distinct from every other cluster, and objects within each cluster are substantially similar to each other [ 75 , 76 , 77 ].…”

Section: Methodsmentioning

confidence: 99%

The Shopping Behavior of International Students in Poland during COVID-19 Pandemic

Szlachciuk

Kulykovets

Dębski

et al. 2022

IJERPH

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The purpose of this research is to analyze the shopping behavior of international students (Asian vs. European) studying in Poland. Participants were recruited from universities located in Warsaw between June and September 2020. A total of 806 questionnaires were collected, 87 of which were eliminated due to non-response. The research sample consisted of 719 people. We conducted an exploratory factor analysis and cluster analysis for the entire study population and separately for European and Asian students. In exploratory factor analysis, two factors were extracted for the entire population, while three factors each were extracted for the European and Asian student groups. In cluster analysis, we obtained four clusters each for the entire study population and the group of European and Asian students. Our study found that among Asian students, compared to European students, there was a greater change in shopping behavior during the COVID-19 pandemic, expressed by a greater preference for online shopping, greater purchases of fruits and vegetables, purchases of local products, and shorter shopping time.

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“…A frequently applied tool that uses unsupervised learning is cluster analysis, which classifies the input data based on some distance metric. Clustering is often applied on geophysical datasets, e.g., for rock typing based on wireline logging data (Ali and Sheng-Chang 2020). Dimension reduction methods also utilize the unsupervised learning approach (e.g., factor analysis or principal component analysis).…”

Section: Machine Learning Tools Used In Geophysicsmentioning

confidence: 99%

Machine learning based approach for the interpretation of engineering geophysical sounding logs

Abordán

Szabó

2021

Acta Geod Geophys

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In this paper, a set of machine learning (ML) tools is applied to estimate the water saturation of shallow unconsolidated sediments at the Bátaapáti site in Hungary. Water saturation is directly calculated from the first factor extracted from a set of direct push logs by factor analysis. The dataset observed by engineering geophysical sounding tools as special variants of direct-push probes contains data from a total of 12 shallow penetration holes. Both one- and two-dimensional applications of the suggested method are presented. To improve the performance of factor analysis, particle swarm optimization (PSO) is applied to give a globally optimized estimate for the factor scores. Furthermore, by a hyperparameter estimation approach, some control parameters of the utilized PSO algorithm are automatically estimated by simulated annealing (SA) to ensure the convergence of the procedure. The result of the suggested ML-based log analysis method is compared and verified by an independent inversion estimate. The study shows that the PSO-based factor analysis aided by hyperparameter estimation provides reliable in situ estimates of water saturation, which may improve the solution of environmental end engineering problems in shallow unconsolidated heterogeneous formations.

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Characterization of well logs using K-mean cluster analysis

Cited by 30 publications

References 21 publications

Cultural Differences in Coping with Changes in the External Environment: A Case of Behavioural Segmentation of Senior Consumers Based on Their Reaction to the COVID-19 Pandemic

Cultural Differences in Coping with Changes in the External Environment: A Case of Behavioural Segmentation of Senior Consumers Based on Their Reaction to the COVID-19 Pandemic

The Shopping Behavior of International Students in Poland during COVID-19 Pandemic

Machine learning based approach for the interpretation of engineering geophysical sounding logs

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