Fair Algorithms for Hierarchical Agglomerative Clustering

Chhabra, Anshuman; Mohapatra, Prasant

doi:10.1109/icmla55696.2022.00036

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Furthermore, these algorithms exhibit two notable benefits compared to non-hierarchical or flat clustering techniques: 1) they do not require an initial specification of the number of clusters; and 2) they generate a hierarchy of all samples in the dataset. [41]. Unsupervised Classification (ISO) We are applying the Maximum Likelihood Classification and ISO Cluster tools on a sequence of input raster bands in order to perform unsupervised Classification.…”

Section: Unsupervised Classification Techniquesmentioning

confidence: 99%

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Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Al-Zubaidi,

Al Khafaji

2024

WJCMS

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Remote sensing, specifically satellite imagery, is gaining prominence in computer science nowadays, in the era of artificial intelligence, in an attempt to deliver more precise information. The satellite images of Earth are gathered, evaluated, and processed for use in civil and military applications with a military aim. Satellite images do have a wide range of services. The areas of study of agriculture, fishery, oceanography, and meteorology include geology, biodiversity, cartography, land use planning, and armed conflict. Transformation is the goal of the categorization of satellite images. Transformation of satellite images into information that can be used rather than having an image of a location. This paper classified a scene of the Landsat-8 satellites with specifications (Path=168 and Row=38). This scene was classified into four categories (Water, Vegetation, bare land, and Build-up) based on the unsupervised classification method (ISO Clustering). The ISO Clustering method is found in the Arc Map program. The results regarding classification accuracy are a good percentage compared to unsupervised Classification.

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Section: Unsupervised Classification Techniquesmentioning

confidence: 99%

“…Riyad Ghafel Abdul Hussein Al Khafaji., Wasit Journal of Computer and Mathematics Science Vol. 3 No.1 (2024) p [40][41][42][43][44][45][46][47][48][49][50]…”

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confidence: 99%

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Al-Zubaidi,

Al Khafaji

2024

WJCMS

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“…, where h denotes the cost in the minimum cost flow they employed. Additionally, fair hierarchical agglomerative clustering, introduced by Chhabra et al [39], has a computational complexity of O(mn 3 ).…”

Section: Computational Complexitymentioning

confidence: 99%

Fairness First Clustering: A Multi-Stage Approach for Mitigating Bias

Pan

Zhong

2023

Electronics

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Fair clustering aims to partition a dataset while mitigating bias in the original dataset. Developing fair clustering algorithms has gained increasing attention from the machine learning community. In this paper, we propose a fair k-means algorithm, fair first clustering (FFC), which consists of an initialization stage, a relaxation stage, and an improvement stage. In the initialization stage, k-means is employed to cluster each group. Then a combination step and a refinement step are applied to ensure clustering quality and guarantee almost fairness. In the relaxation stage, a commonly used fairness metric, balance, is utilized to assess fairness, and a threshold is set to allow for fairness relaxation while improving the clustering quality. In the improvement stage, a local search method is used to improve the clustering quality without changing the fairness. Comparisons of fairness and clustering quality are carried out between our method and other state-of-the-art fair clustering methods on 10 datasets, which include both synthetic and real-world datasets. The results show that compared to the method with the second highest balance value, FFC shares the same SSE value on one dataset and achieves lower SSE values on six datasets.

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MAP-FCRNN: Multi-step ahead prediction model using forecasting correction and RNN model with memory functions

Zhang

Ding

et al. 2023

Information Sciences

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Fair Algorithms for Hierarchical Agglomerative Clustering

Cited by 6 publications

References 12 publications

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Fairness First Clustering: A Multi-Stage Approach for Mitigating Bias

MAP-FCRNN: Multi-step ahead prediction model using forecasting correction and RNN model with memory functions

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