Clustering Techniques in Data Mining: A Comparative Analysis

Reddy, E. Kesavulu

doi:10.9734/bpi/mono/978-93-5547-265-6/ch9

Cited by 2 publications

(1 citation statement)

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“…Depending upon the chosen metric, a data object may belong to a single cluster or more than one cluster. Actually, the choice of selecting the clustering algorithm is a critical step of the process [23], [24]. The optimal number of clusters can be determined by different mathematical criteria aiming to measure and identify some similarities among the data objects.…”

Section: Methodsmentioning

confidence: 99%

Characterization of Vertical Wind Speed Profiles Based on Ward’s Agglomerative Clustering Algorithm

C. Bueso,

Molina-Garcia,

P. Ramallo-Gonzalez

et al. 2023

Journal of Modern Power Systems and Clean Energy

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Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward' s agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee (FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

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Section: Methodsmentioning

confidence: 99%

Characterization of Vertical Wind Speed Profiles Based on Ward’s Agglomerative Clustering Algorithm

C. Bueso,

Molina-Garcia,

P. Ramallo-Gonzalez

et al. 2023

Journal of Modern Power Systems and Clean Energy

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Recognising small colour changes with unsupervised learning, comparison of methods

Isohanni

2024

Adv. in Comp. Int.

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Colour differentiation is crucial in machine learning and computer vision. It is often used when identifying items and objects based on distinct colours. While common colours like blue, red, green, and yellow are easily distinguishable, some applications require recognising subtle colour variations. Such demands arise in sectors like agriculture, printing, healthcare, and packaging. This research employs prevalent unsupervised learning techniques to detect printed colours on paper, focusing on CMYK ink (saturation) levels necessary for recognition against a white background. The aim is to assess whether unsupervised clustering can identify colours within QR-Codes. One use-case for this research is usage of functional inks, ones that change colour based on environmental factors. Within QR-Codes they serve as low-cost IoT sensors. Results of this research indicate that K-means, C-means, Gaussian Mixture Model (GMM), Hierarchical clustering, and Spectral clustering perform well in recognising colour differences when CMYK saturation is 20% or higher in at least one channel. K-means stands out when saturation drops below 10%, although its accuracy diminishes significantly, especially for yellow or magenta channels. A saturation of at least 10% in one CMYK channel is needed for reliable colour detection using unsupervised learning. To handle ink densities below 5%, further research or alternative unsupervised methods may be necessary.

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Clustering Techniques in Data Mining: A Comparative Analysis

Cited by 2 publications

References 0 publications

Characterization of Vertical Wind Speed Profiles Based on Ward’s Agglomerative Clustering Algorithm

Characterization of Vertical Wind Speed Profiles Based on Ward’s Agglomerative Clustering Algorithm

Recognising small colour changes with unsupervised learning, comparison of methods

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