Scalable Active Constrained Clustering for Temporal Data

T., Son; Amer-Yahia, Sihem; Douzal-Chouakria, Ahlame; Nguyen, Ky T. Nguyen And Anh-Duong

doi:10.1007/978-3-319-91452-7_37

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…The main idea behind CVQE is to help guide the formation of clusters to satisfy certain requirements while still maintaining the flexibility of unsupervised learning. This approach was seen as innovative and has since been improved with methods such as LCVQE (Pelleg & Baras, 2007) and CVQE+ (Mai et al, 2018). These improvements have further enhanced the algorithm's performance, making it a promising approach for clustering tasks.…”

Section: Related Workmentioning

confidence: 99%

Constrained Density Peak Clustering

Vu,

Bui,

Nguyen

et al. 2023

International Journal of Data Warehousing and Mining

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Clustering is a commonly used tool for discovering knowledge in data mining. Density peak clustering (DPC) has recently gained attention for its ability to detect clusters with various shapes and noise, using just one parameter. DPC has shown advantages over other methods, such as DBSCAN and K-means, but it struggles with datasets that have both high and low-density clusters. To overcome this limitation, the paper introduces a new semi-supervised DPC method that improves clustering results with a small set of constraints expressed as must-link and cannot-link. The proposed method combines constraints and a k-nearest neighbor graph to filter out peaks and find the center for each cluster. Constraints are also used to support label assignment during the clustering procedure. The efficacy of this method is demonstrated through experiments on well-known data sets from UCI and benchmarked against contemporary semi-supervised clustering techniques.

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Section: Related Workmentioning

confidence: 99%

Constrained Density Peak Clustering

Vu,

Bui,

Nguyen

et al. 2023

International Journal of Data Warehousing and Mining

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“…In this paper, we extend the work in [27] for investigating clinical clusters in a use case of patients with Obstructive Sleep Apnea (OSA) [22]. OSA is a common sleep disorder and a major medical concern corresponding to repetitive upper airway collapse during sleep [22,44].…”

Section: Extensionsmentioning

confidence: 99%

“…proposed in our previous works [27] is that each object inside a snapshot is assigned a unique Id. Two objects in two different snapshots may have the same Id as described in Sect.…”

Section: Temporal Smoothnessmentioning

confidence: 99%

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Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis

Amer-Yahia

Bailly

et al. 2018

Data Sci. Eng.

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We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large longitudinal data and for tracking the cluster evolutions over time. It consists of a constrained clustering algorithm, called CVQE+, which optimizes the clustering quality, constraint violation and the historical cost between consecutive data snapshots. At the center of our framework is a simple yet effective active learning technique, named Border, for iteratively selecting the most informative pairs of objects to query users about, and updating the clustering with new constraints. Those constraints are then propagated inside each data snapshot and between snapshots via two schemes, called constraint inheritance and constraint propagation, to further enhance the results. Moreover, a historical constraint is enforced between consecutive snapshots to ensure the consistency of results among them. Experiments show better or comparable clustering results than state-of-the-art techniques as well as high scalability for large datasets. Finally, we apply our algorithm for clustering phenotypes in patients with Obstructive Sleep Apnea as well as for tracking how these clusters evolve over time.

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Interactive Exploration of Subspace Clusters on Multicore Processors

Kristensen

Assent

et al. 2018

Transactions on Large-Scale Data- And Knowledge-Centered Systems XXXIX

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Scalable Active Constrained Clustering for Temporal Data

Cited by 4 publications

References 14 publications

Constrained Density Peak Clustering

Constrained Density Peak Clustering

Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis

Interactive Exploration of Subspace Clusters on Multicore Processors

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