A Survey of Constrained Clustering

Dinler, Derya; Tural, Mustafa Kemal

doi:10.1007/978-3-319-24211-8_9

Cited by 23 publications

(27 citation statements)

References 42 publications

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“…1. During the calculation process of the Hausdorff distance betweenx i andx j , we can easily find these two points that measure the distance ofx i and x j in order to simplify (9). We denote them as x u and x f , where x u ∈x i and x f ∈x j .…”

Section: B Linear Group-based Distance Learningmentioning

confidence: 99%

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A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering

Jing

Zheng

Shen

et al. 2022

IEEE Trans. Cybern.

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Learning a proper distance for clustering from prior knowledge falls into the realm of semisupervised fuzzy clustering. Although most existing learning methods take prior knowledge (e.g., pairwise constraints) into account, they pay little attention to local knowledge of data, which, however, can be utilized to optimize the distance. In this article, we propose a novel distance learning method, which learns from the Group-level information, for semisupervised fuzzing clustering. We first present a new format of constraint information, called Group-level constraints, by elevating the pairwise constraints (must-links and cannotlinks) from point level to Group level. The Groups, generated around data points contained in the pairwise constraints, carry not only the local information of data (the relation between close data points) but also more background information under some given limited prior knowledge. Then, we propose a novel method to learn a distance by using the Group-level constraints, namely, Group-based distance learning, in order to optimize the performance of fuzzy clustering. The distance learning process aims to pull must-link Groups as close as possible while pushing cannot-link Groups as far as possible. We formulate the learning process with the weights of constraints by invoking some linear and nonlinear transformations. The linear Group-based distance learning method is realized by means of semidefinite programming, and the nonlinear learning method is realized by Manuscript

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Section: B Linear Group-based Distance Learningmentioning

confidence: 99%

“…This is the main goal of semisupervised clustering [8]. The prior knowledge mainly comprises [9] pairwise constraints (must-links and cannot-links); class labels; clusters' position or identity; the size of clusters; proximity knowledge [10], [11]; and partition-level information [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering

Jing

Zheng

Shen

et al. 2022

IEEE Trans. Cybern.

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“…There are survey works giving a landscape on clustering techiques [44][45][46][47][48][49][50], where, most of the works divide clustering algorithms on the basis of their paradigms. Thus, the most important clustering paradigms reported in the literature are: Partitional Clustering, Hierarchical Clustering, Densitybased Clustering, Spectral Clustering, and Gravitational Clustering, which are summarized in Table 1.…”

Section: State-of-the-artmentioning

confidence: 99%

Topology: A Theory of a Pseudometric‐Based Clustering Model and Its Application in Content‐Based Image Retrieval

Osuna-Galán

Pérez-Pimentel

Avilés‐Cruz

et al. 2019

Mathematical Problems in Engineering

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The clustering problem has been extensively studied over the last 50 years; however, it still has the attention of researchers. This paper presents a topological basis of a pseudometric-based clustering model which takes into account the local and global topological properties of the data to be clustered, as per the definition of homogeneity measurement. The proposed approach takes into account the homogeneity effect produced when a new particle is added to a group. The additional element can be accumulated in the group if its local homogeneity is not altered and, therefore, it is not necessary to carry out tests in another group. A new group needs to be generated if the threshold of the local homogeneity of the group exceeds. Theoretical results, their implementation, and their application to the problem of Content Based Image Retrieval (CBIR) are presented. The tests were performed using three image databases widely used in the literature, which are “Vogel and Shiele,” “Oliva and Torralba,” and “L. Fei- Fei, R. Fergus and P. Perona.” The results are presented and compared with the most competitive methods available in the literature.

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“…Specifically methods merely based on distance metrics such as euclidean distance or dynamic time warping [4] can not capture structural similarities based on correlations across time. For static data, there has been a growing interest in semi-supervised clustering methods, for example constrained clustering, where additional a priori information or domain knowledge is incorporated into the clustering process, to better capture complex relations between features [5][6][7]. In general semi-supervised clustering algorithms can be divided into two groups, pointwise and pairwise algorithms, where the former has pre-labeled points available and the latter is usually expressed in must-link and cannot-link constraints [8,Chapter 20,Agovic et al].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Time Point Clustering for Multivariate Time Series

Ertl

Meyer

Schneider

et al. 2021

Proceedings of the Canadian Conference on Artificial Intelligence

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The formation and analysis of clusters in multivariate time series can reveal interesting patterns and complex correlations in temporal data. However, traditional clustering methods based on distance metrics fall short to discover interpretable characteristics and structures reflected by these clusters. This paper provides a new method for semisupervised time point clustering based on the temporal proximity of time points and the correlation of their corresponding values. For this purpose, we utilize CoExDB-SCAN, a recently developed density-based clustering algorithm with constrained expansion. CoExDBSCAN allows to identify clusters of temporal neighbourhoods that are only expanded with regards to a priori constraints in defined subspaces. Adopting this algorithm to time series data and grouping segments with similar correlations allows us to find accurate and interpretable structures. We provided a comparison to state-of-theart methods and verification of our approach on a synthetic dataset and an experimental evaluation on a real-world dataset. The experimental assessment shows that our clustering results can further serve as an effective basis for time series classification.

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A Survey of Constrained Clustering

Cited by 23 publications

References 42 publications

A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering

A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering

Topology: A Theory of a Pseudometric‐Based Clustering Model and Its Application in Content‐Based Image Retrieval

Semi-Supervised Time Point Clustering for Multivariate Time Series

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