Constrained distance based clustering for time-series: a comparative and experimental study

Lampert, Thomas; Dao, Thi-Bich-Hanh; Lafabregue, Baptiste; Serrette, Nicolas; Forestier, Germain; Crémilleux, Bruno; Vrain, Christel; Gançarski, Pierre

doi:10.1007/s10618-018-0573-y

Cited by 25 publications

(20 citation statements)

References 94 publications

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“…The relevance of pairwise constraints in semi-supervised clustering has been thoroughly investigated in the literature-e.g. see(Lampert et al 2018) for a recent study in the realm of time-series.…”

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confidence: 99%

A unified view of density-based methods for semi-supervised clustering and classification

Gertrudes

Zimek

Sander

et al. 2019

Data Min Knowl Disc

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Semi-supervised learning is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatically increasing. In this paper, we first introduce a unified view of density-based clustering algorithms. We then build upon this view and bridge the areas of semi-supervised clustering and classification under a common umbrella of density-based techniques. We show that there are close relations between density-based clustering algorithms and the graph-based approach for transductive classification. These relations are then used as a basis for a new framework for semi-supervised classification based on building-blocks from density-based clustering. This framework is not only efficient and effective, but it is also statistically sound. In addition, we generalize the core algorithm in our framework, HDBSCAN*, so that it can also perform semi-supervised clustering by directly taking advantage of any fraction of labeled data that may be available. Experimental results on a large collection of datasets show the advantages of the proposed approach both for semi-supervised classification as well as for semi-supervised clustering.

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confidence: 99%

A unified view of density-based methods for semi-supervised clustering and classification

Gertrudes

Zimek

Sander

et al. 2019

Data Min Knowl Disc

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“…Also, they showed that K-medoids is better in terms of execution time, non sensitive to outliers, reduces noise and minimizes the sum of dissimilarities of data objects. Lampert et al [21] also showed the limits of the K-means algorithm under some constraints.…”

Section: Related Workmentioning

confidence: 99%

Contextual data classification for a ubiquitous intelligent environment

et al. 2020

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Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The K-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.

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“…Constrained clustering algorithms broadly fall into six categories: k-Means, Metric Learning, Spectral Graph Theory, Ensemble, Collaborative, and Declarative. An in-depth review of all appraoches is presented in [5]. This work focusses on k-Means approaches, including one example from collaborative clustering, as they are ubiquitous in remote sensing.…”

Section: Constrained Clusteringmentioning

confidence: 99%

Constrained Distance based K-Means Clustering for Satellite Image Time-Series

Lampert

Lafabregue

Gançarski

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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The advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available k-Means constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is thought to be more appropriate for time-series analysis. Adding constraints to the clustering problem increases accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions.

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Constrained distance based clustering for time-series: a comparative and experimental study

Cited by 25 publications

References 94 publications

A unified view of density-based methods for semi-supervised clustering and classification

A unified view of density-based methods for semi-supervised clustering and classification

Contextual data classification for a ubiquitous intelligent environment

Constrained Distance based K-Means Clustering for Satellite Image Time-Series

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