Interactive Machine Learning Tool for Clustering in Visual Analytics

Thrun, Michael C.; Pape, Felix; Ultsch, Alfred

doi:10.1109/dsaa49011.2020.00062

Cited by 10 publications

(10 citation statements)

References 25 publications

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“…In this work, IPBC is compared to publicly available visual analytic methods for clustering artificial and high-dimensional datasets from real-world experiments. This work is an extension of the manuscript initially presented at DSAA 2020 to four examples [13].…”

Section: Introductionmentioning

confidence: 98%

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Thrun

Pape

Ultsch

2021

Int J Data Sci Anal

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Clustering is an important task in knowledge discovery with the goal to identify structures of similar data points in a dataset. Here, the focus lies on methods that use a human-in-the-loop, i.e., incorporate user decisions into the clustering process through 2D and 3D displays of the structures in the data. Some of these interactive approaches fall into the category of visual analytics and emphasize the power of such displays to identify the structures interactively in various types of datasets or to verify the results of clustering algorithms. This work presents a new method called interactive projection-based clustering (IPBC). IPBC is an open-source and parameter-free method using a human-in-the-loop for an interactive 2.5D display and identification of structures in data based on the user’s choice of a dimensionality reduction method. The IPBC approach is systematically compared with accessible visual analytics methods for the display and identification of cluster structures using twelve clustering benchmark datasets and one additional natural dataset. Qualitative comparison of 2D, 2.5D and 3D displays of structures and empirical evaluation of the identified cluster structures show that IPBC outperforms comparable methods. Additionally, IPBC assists in identifying structures previously unknown to domain experts in an application.

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Section: Introductionmentioning

confidence: 98%

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Thrun

Pape

Ultsch

2021

Int J Data Sci Anal

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show abstract

“…Contrary to the approaches introduced above, in which an obscure global objective function is optimized, this work uses the topographic map visualization [41] with projectibased clustering [42] to validate if the data contains any or no structures based on clusters. Outliers can be interactively marked in the visualization [43] after the automated clustering process in the case that they are not recognized sufficiently in the automatic clustering process [43]. The clustering itself is confined in one module of the XAI framework presented in Figure 1.…”

Section: Xai Frameworkmentioning

confidence: 99%

“…process [43]. The clustering itself is confined in one module of the XAI framework presented in Figure 1.…”

Section: Xai Frameworkmentioning

confidence: 99%

“…Additionally, the clustering can be improved further using an interactive interface (c.f. provided in the R package 'ProjectionBasedClustering') [43].…”

Section: Verification Of a Clusteringmentioning

confidence: 99%

“…No structures are visible on the topographic maps. [42,43] https://CRAN.R-project.org/ package=ProjectionBasedClustering FeatureImpCluster "Implements a novel approach for measuring feature importance in k-means clustering" [40] https://CRAN.R-project.org/ package=FeatureImpCluster…”

Section: Data Availability Statementmentioning

confidence: 99%

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The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented.

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Primary Healthcare Model for Remote Area Using Self‐Organizing Map Network

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Computational Intelligence and Healthcare Informatics

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Interactive Machine Learning Tool for Clustering in Visual Analytics

Cited by 10 publications

References 25 publications

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Explainable AI Framework for Multivariate Hydrochemical Time Series

Primary Healthcare Model for Remote Area Using Self‐Organizing Map Network

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