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Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets

Connor Clarkson, Mike Edwards Orcid Logo, Xianghua Xie Orcid Logo

Lecture Notes in Computer Science, Volume: 15656, Pages: 251 - 263

Swansea University Authors: Connor Clarkson, Mike Edwards Orcid Logo, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1007/978-3-032-07343-3_20

Abstract

Many supervised-based training schemes rely on the need to have a single associated label for each data sample within a set. Where the goal is to learn different levels of granularity of the data in an implicit form, in the context of neural networks, this is accomplished with different layers to ex...

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Published in: Lecture Notes in Computer Science
ISBN: 9783032073426 9783032073433
ISSN: 0302-9743 1611-3349
Published: Cham Springer Nature Switzerland 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa70372
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spelling 2026-01-08T17:12:11.3145298 v2 70372 2025-09-17 Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets e1a00716a3866cd4d8bb0ade1bada119 Connor Clarkson Connor Clarkson true false 684864a1ce01c3d774e83ed55e41770e 0000-0003-3367-969X Mike Edwards Mike Edwards true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2025-09-17 MACS Many supervised-based training schemes rely on the need to have a single associated label for each data sample within a set. Where the goal is to learn different levels of granularity of the data in an implicit form, in the context of neural networks, this is accomplished with different layers to extract features at distinct levels. In this work, we explore more explicit labelling structures where each sample has multiple labels forming relationships at both an abstract and fine-grained level, producing a tree for each associated data sample. This novel type of training scheme utilises a refinement strategy based on deep clustering approaches to detect the colliding and splitting of clusters where each is assigned a label. Experts can then be queried to determine if those colliding clusters should belong to a single label, or alternatively, if they are splitting, should we create new labels, forming an active component of our training scheme. Colliding clusters form a parent label while splitting clusters form sibling labels within the tree structure. By utilizing a tree data structure to represent labels at different levels of granularity, we can invoke explicitly defined relationships and dependencies to form a more structured and interpretable representation of data. Instead of treating data as flat, homogenous sets, we allow for the exploitation of hierarchical relationships and leverage inherent structure to improve data efficiency. We present a case study of the approach applied within the steel manufacturing domain, where quality control remains an active challenge due to morphing labels as products move down the production line. Book chapter Lecture Notes in Computer Science 15656 251 263 Springer Nature Switzerland Cham 9783032073426 9783032073433 0302-9743 1611-3349 Dataset Refinement; Training Schemes; Tree-based Modelling 2 1 2026 2026-01-02 10.1007/978-3-032-07343-3_20 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2026-01-08T17:12:11.3145298 2025-09-17T11:55:46.2153113 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Connor Clarkson 1 Mike Edwards 0000-0003-3367-969X 2 Xianghua Xie 0000-0002-2701-8660 3 70372__35100__859e7120214042928f71c1612b3f822f.pdf ACIVS_ADC_Exploratory_Analysis_to_Assist_in_Decision_Making_on_Incremental_Label_Morphing_Datasets.pdf 2025-09-17T12:04:47.3399516 Output 1605122 application/pdf Accepted Manuscript true 2025-11-24T00:00:00.0000000 Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
spellingShingle Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
Connor Clarkson
Mike Edwards
Xianghua Xie
title_short Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
title_full Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
title_fullStr Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
title_full_unstemmed Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
title_sort Active Deep Clustering: Exploratory Analysis to Assist in Decision-Making on Incremental Label Morphing Datasets
author_id_str_mv e1a00716a3866cd4d8bb0ade1bada119
684864a1ce01c3d774e83ed55e41770e
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author_id_fullname_str_mv e1a00716a3866cd4d8bb0ade1bada119_***_Connor Clarkson
684864a1ce01c3d774e83ed55e41770e_***_Mike Edwards
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Connor Clarkson
Mike Edwards
Xianghua Xie
author2 Connor Clarkson
Mike Edwards
Xianghua Xie
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doi_str_mv 10.1007/978-3-032-07343-3_20
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description Many supervised-based training schemes rely on the need to have a single associated label for each data sample within a set. Where the goal is to learn different levels of granularity of the data in an implicit form, in the context of neural networks, this is accomplished with different layers to extract features at distinct levels. In this work, we explore more explicit labelling structures where each sample has multiple labels forming relationships at both an abstract and fine-grained level, producing a tree for each associated data sample. This novel type of training scheme utilises a refinement strategy based on deep clustering approaches to detect the colliding and splitting of clusters where each is assigned a label. Experts can then be queried to determine if those colliding clusters should belong to a single label, or alternatively, if they are splitting, should we create new labels, forming an active component of our training scheme. Colliding clusters form a parent label while splitting clusters form sibling labels within the tree structure. By utilizing a tree data structure to represent labels at different levels of granularity, we can invoke explicitly defined relationships and dependencies to form a more structured and interpretable representation of data. Instead of treating data as flat, homogenous sets, we allow for the exploitation of hierarchical relationships and leverage inherent structure to improve data efficiency. We present a case study of the approach applied within the steel manufacturing domain, where quality control remains an active challenge due to morphing labels as products move down the production line.
published_date 2026-01-02T05:32:43Z
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