A Comparative Study of Clustering Algorithm

Shrikant, Khyaati; Gupta, Vaishnavi; Khandare, Anand; Furia, Palak

doi:10.1007/978-981-16-4863-2_19

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Relying solely on general scores is definitely not sufficient. For instance, using the Dunn index or the Davies–Bouldin index can reduce the granularity of posture differentiation due to data distribution sensitivities [ 24 , 31 , 54 , 55 , 56 ]. The proposed discriminant score provides a dynamic perspective on posture classification over time, emphasizing the importance of selecting an adequate number of clusters that preserve the data structure details.…”

Section: Discussionmentioning

confidence: 99%

“…It offers a balance between computational efficiency, granularity of postural representation, and distinction, effectively addressing major challenges in the field. However, evaluating clustering quality using CVIs can be influenced by factors such as data distribution [ 24 , 31 , 54 ], unbalanced cluster representation, and diverse recommendations from different CVIs. They sometimes suggest different optimal cluster numbers, which adds to the complexity of determining the best decision based on the data distribution.…”

Section: Evaluation Of Clustering Qualitymentioning

confidence: 99%

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Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Ekemeyong Awong,

Zielinska

2023

Sensors

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The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores—such as the silhouette coefficient, Dunn index, Calinski–Harabasz index, Davies–Bouldin index, and DS—for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.

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Section: Discussionmentioning

confidence: 99%

Section: Evaluation Of Clustering Qualitymentioning

confidence: 99%

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Ekemeyong Awong,

Zielinska

2023

Sensors

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“…Clustering, an unsupervised learning method, involves organizing unlabeled data points into groups based on their similarities, aiming to identify inherent structures or patterns within the data [33]. Hierarchical and non-hierarchical methods are two commonly used clustering methods [34][35][36].…”

Section: Clustering Methodsmentioning

confidence: 99%

Climate Classification for Major Cities in China Using Cluster Analysis

Duan,

Li,

et al. 2024

Atmosphere

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Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China’s traditional geographical division along the Qinling–Huaihe River line.

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