Multiscale visual quality assessment for cluster analysis with self-organizing maps

Bernard, Jürgen; Landesberger, Tatiana von; Bremm, Sebastian; Schreck, Tobias

doi:10.1117/12.872545

Cited by 6 publications

(5 citation statements)

References 22 publications

(30 reference statements)

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“…The U-matrix is often used to explore the parameters' interactions between the SOM's formed groups (clusters) [47]. The U-matrix visualization (Figure 2) indicated a tendency for the data to be grouped into three clusters; however, this was not clearly observed here (see Figure 2).…”

Section: Som's Resultsmentioning

confidence: 83%

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

Hadjisolomou,

Rousou,

Antoniadis

et al. 2023

Water

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Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxin production. Monitoring coastal eutrophication is crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the tourist sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in recent decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modeling study, two different types of artificial neural networks (ANNs) are developed based on in situ data collected from stations located in the coastal waters of Cyprus. These ANNs aim to model the eutrophication phenomenon based on two different data-driven modeling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters’ (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen, and electrical conductivity) interactions with the Chlorophyll-a (Chl-a) parameter. The SOM model enables us to visualize the monitored parameters’ relationships and to comprehend complex biological mechanisms related to Chl-a production. A second feed-forward ANN model is also developed for predicting the Chl-a levels. The feed-forward ANN managed to predict the Chl-a levels with great accuracy (MAE = 0.0124; R = 0.97). The sensitivity analysis results revealed that salinity and water temperature are the most influential parameters on Chl-a production. Moreover, the sensitivity analysis results of the feed-forward ANN captured the winter upwelling phenomenon that is observed in Cypriot coastal waters. Regarding the SOM results, the clustering verified the oligotrophic nature of Cypriot coastal waters and the good water quality status (only 1.4% of the data samples were classified as not good). The created ANNs allowed us to comprehend the mechanisms related to eutrophication regarding the coastal waters of Cyprus and can act as useful management tools regarding eutrophication control.

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Section: Som's Resultsmentioning

confidence: 83%

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

Hadjisolomou,

Rousou,

Antoniadis

et al. 2023

Water

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“…The SOM algorithm is used to enable users to analyze and partition large unknown data collections. Our decision for using the SOM is based on its special characteristics conflating data clustering, vector quantization, dimension reduction, and the ability for cluster visualization [12,31,46]. Hence, we make use of existing techniques to support the analysis of the SOM grid visually.…”

Section: Analyze Visual Results (Single Som)mentioning

confidence: 99%

“…We are also aware of SOM-based quality measures to calculate, e.g., quantization errors within cells, or topological errors [31,36]. It is further possible to visually asses the quality of a SOM result [12] using SOM-grid/network visualizations, such as the u-matrix [50], or s-map (smoothed data histograms) [35]. Further work by Bernard et al [9,10] describes approaches to measure the strength of relations between data content and metadata, such as using Simpson's diversity or Shannon entropy measures.…”

Section: Quality-based Guidance For Visual Explorationmentioning

confidence: 99%

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance

Sacha

Kraus

Bernard

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

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“…SOM algorithm is used to explore partitions and visualize students' dataset based on its distinctive support for data clustering, vector quantization, dimension reduction, and cluster visualization capabilities (Bernard, et al, 2011, Kohonen, et al, 2001. SOM was applied to identify structures and classify the students' dataset into the segments with similar performance characteristics.…”

Section: Som Visualizationmentioning

confidence: 99%

Unsupervised Characterization and Visualization of Students’ Academic Performance Features

Inyang¹,

Umoh²,

Nnaemeka³

et al. 2019

CIS

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The large nature of students’ dataset has made it difficult to find patterns associated with students’ academic performance (AP) using conventional methods. This has increased the rate of drop-outs, graduands with weak class of degree (CoD) and students that spend more than the minimum stipulated duration of studies. It is necessary to determine students’ AP using educational data mining (EDM) tools in order to know students who are likely to perform poorly at an early stage of their studies. This paper explores k-means and self-organizing map (SOM) in mining pieces of knowledge relating to the natural number of clusters in students’ dataset and the association of the input features using selected demographic, pre-admission and first year performance. Matlab 2015a was the programming environment and the dataset consists of nine sets of computer science graduands. Cluster validity assessment with k-means discovered four (4) clusters with correlation metric yielding the highest mean silhouette value of 0.5912.  SOM provided an hexagonal grid visual of feature component planes and scatter plots of each significant input attribute. The result shows that the significant attributes were highly correlated with each other except entry mode (EM), indicating that the impact of EM on CoD varies with students irrespective of mode of admission. Also, four distinct clusters were also discovered in the dataset by SOM —7.7% belonging to cluster 1 (first class), and 25% for cluster 2 (2nd class Upper) while Clusters 3 and 4 had 35% proportion each. This validates the results of k-means and further confirms the importance of early detection of students’ AP and confirms the effectiveness of SOM as a cluster validity tool. As further work, the labels from SOM will be associated with records in the dataset for association rule mining, supervised learning and prediction of students’ AP.

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Multiscale visual quality assessment for cluster analysis with self-organizing maps

Cited by 6 publications

References 22 publications

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance

Unsupervised Characterization and Visualization of Students’ Academic Performance Features

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