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XplainableClusterExplorer: a novel approach for interactive feature selection for clustering

Authors:

Andreas TheisslerAuthors Info & Claims

VINCI '20: Proceedings of the 13th International Symposium on Visual Information Communication and Interaction

Article No.: 15, Pages 1 - 5

https://doi.org/10.1145/3430036.3430066

Published: 08 December 2020 Publication History

Abstract

Human-centered machine learning is becoming an emerging field aiming to enable domain experts that do not necessarily have a data science background to make use of machine learning applications. Especially in unsupervised machine learning, e.g. cluster analysis, models cannot be autonomously tuned towards an optimal solution for a given application due to the absence of ground truth like class labels. In cluster analysis, different feature subsets may lead to different clusterings. The identification of the best subset of given features is therefore essential in order to improve the overall clustering performance and to obtain a clustering that is suitable for a given application. To support users in finding an optimal clustering solution, we propose XplainableClusterExplorer, an interactive and explorative approach suitable for feature selection for clustering. In an interactive combination of user and machine learning models, the user is supported by evaluation criteria and visualizations in determining feature subsets and adjusting hyperparameters. For feature subset selection we propose a combination with feature importances from random forests and LIME. Since this requires a supervised setting, the cluster assignments are used as tentative class labels in subsequent step. Our experimental results have shown that this subsequent classification step leveraging calculated feature importances can facilitate feature subset selection and therefore enhance overall clustering performance.

References

[1]

Salem Alelyani, Jiliang Tang, and Huan Liu. 2013. Feature Selection for Clustering: A Review. In Data Clustering, Charu C. Aggarwal and Chandan K. Reddy (Eds.). Chapman and Hall/CRC, Boca Raton, FL, 29--60.

[2]

Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5--32.

Digital Library

[3]

Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz, and Cyrus Rashtchian. 2020. Explainable k-Means and k-Medians Clustering. arXiv preprint arXiv:2002.12538 (2020).

[4]

Manoranjan Dash, Kiseok Choi, Peter Scheuermann, and Huan Liu. 2002. Feature Selection for Clustering - A Filter Solution. (2002).

[5]

David L. Davies and Donald W. Bouldin. 1979. A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1, 2 (1979), 224--227.

Digital Library

[6]

Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml

[7]

J. C. Dunn. 1973. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3, 3 (1973), 32--57.

[8]

Jennifer G. Dy and Carla E. Brodley. 2000. Visualization and interactive feature selection for unsupervised data. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, Raghu Ramakrishnan (Ed.). ACM, New York, NY, 360--364.

Digital Library

[9]

Sanjay Goil, Harsha Nagesh, and Alok Choudhary. 1999. MAFIA: Efficient and scalable subspace clustering for very large data sets. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Vol. 443. ACM, 452.

[10]

Benedikt Grimmeisen and Andreas Theissler. 2020. The Machine Learning Model as a Guide: Pointing Users to Interesting Instances for Labeling through Visual Cues. In The 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020), December 8--10, 2020, Eindhoven, Netherlands. ACM.

Digital Library

[11]

Diansheng Guo. 2003. Coordinating Computational and Visual Approaches for Interactive Feature Selection and Multivariate Clustering. Information Visualization 2, 4 (2003), 232--246.

Digital Library

[12]

Emrah Hancer, Bing Xue, and Mengjie Zhang. 2020. A survey on feature selection approaches for clustering. Artificial Intelligence Review 53, 6 (2020), 4519--4545.

Digital Library

[13]

Andreas Holzinger. 2015. Interactive Machine Learning (iML). Informatik-Spektrum 39 (2015).

[14]

Plotly Technologies Inc. 2015. Collaborative data science. Montreal, QC. https://plot.ly

[15]

R Development Core Team. 2010. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ ISBN 3-900051-07-0.

[16]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. [n.d.]. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. https://arxiv.org/pdf/1602.04938

[17]

Peter J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20 (1987), 53--65.

Digital Library

[18]

Sobar, Rizanda Machmud, and Adi Wijaya. 2016. Behavior Determinant Based Cervical Cancer Early Detection with Machine Learning Algorithm. Advanced Science Letters 22, 10 (2016), 3120--3123.

[19]

Andreas Theissler, Anna-Lena Kraft, Max Rudeck, and Fabian Erlenbusch. 2020. VIAL-AD: Visual Interactive Labelling for Anomaly Detection - An approach and open research questions. In 4th International Workshop on Interactive Adaptive Learning (IAL2020). CEUR-WS.

[20]

Andreas Theissler, Simon Vollert, Patrick Benz, Laurentius A Meerhoff, and Marc Fernandes. 2020. ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction. Springer, 281--300.

Digital Library

[21]

Robert Tibshirani, Guenther Walther, and Trevor Hastie. 2001. Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, 2 (2001), 411--423.

[22]

B. Wang and K. Mueller. 2018. The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces. IEEE Transactions on Visualization and Computer Graphics 24, 2 (2018), 1204--1222.

[23]

X. Yuan, D. Ren, Z. Wang, and C. Guo. 2013. Dimension Projection Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2625--2633.

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Cited By

Ahn H(2023)Performance Evaluation of a Feature-Importance-based Feature Selection Method for Time Series PredictionJournal of information and communication convergence engineering10.56977/jicce.2023.21.1.8221:1(82-89)Online publication date: 31-Mar-2023
https://doi.org/10.56977/jicce.2023.21.1.82
Grimmeisen BChegini MTheissler A(2022)VisGIL: machine learning-based visual guidance for interactive labelingThe Visual Computer10.1007/s00371-022-02648-239:10(5097-5119)Online publication date: 25-Sep-2022
https://doi.org/10.1007/s00371-022-02648-2

Index Terms

XplainableClusterExplorer: a novel approach for interactive feature selection for clustering
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
2. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics

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Information & Contributors

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Published In

cover image ACM Other conferences

VINCI '20: Proceedings of the 13th International Symposium on Visual Information Communication and Interaction

December 2020

205 pages

ISBN:9781450387507

DOI:10.1145/3430036

General Chairs:
Michael Burch
Eindhoven University of Technology, The Netherlands
,
Michel Westenberg
Eindhoven University of Technology, The Netherlands
,
Program Chairs:
Quang Vinh Nguyen
Western Sydney University, Australia
,
Ying Zhao
Central South University, China

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 December 2020

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VINCI 2020

VINCI 2020: The 13th International Symposium on Visual Information Communication and Interaction

December 8 - 10, 2020

Eindhoven, Netherlands

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Cited By

Ahn H(2023)Performance Evaluation of a Feature-Importance-based Feature Selection Method for Time Series PredictionJournal of information and communication convergence engineering10.56977/jicce.2023.21.1.8221:1(82-89)Online publication date: 31-Mar-2023
https://doi.org/10.56977/jicce.2023.21.1.82
Grimmeisen BChegini MTheissler A(2022)VisGIL: machine learning-based visual guidance for interactive labelingThe Visual Computer10.1007/s00371-022-02648-239:10(5097-5119)Online publication date: 25-Sep-2022
https://doi.org/10.1007/s00371-022-02648-2

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