Constrained clustering with a complex cluster structure

Śmieja, Marek; Wiercioch, Magdalena

doi:10.1007/s11634-016-0254-x

Cited by 15 publications

(8 citation statements)

References 40 publications

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“…Melnykov et al (2016) proposed a method for clustering with constraints by incorporating the additional information into the EM algorithm traditionally used in mixture modeling and model-based clustering. Smieja and Wiercioch (2017) proposed 294 IJICC 12,2 constrained clustering with a complex cluster structure (C4s). The idea of C4s relies on the observation that every chunklet that is defined as a sub-set of points that are known to belong to the same class can originate from complex model, e.g.…”

Section: Constraint-based Methods In Constrained Clusteringmentioning

confidence: 99%

Clustering in the presence of side information: a non-linear approach

Abin

2019

IJICC

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Purpose Constrained clustering is an important recent development in clustering literature. The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background knowledge. The purpose of this paper is to suggest a new perspective for constrained clustering, by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must- and cannot-link constraints. Design/methodology/approach Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target space. Unlike previous efforts, the author presents a non-linear method for constraint clustering, whose basic idea is to use different non-linear functions for each dimension in target space. Findings The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linear functions for each dimension in target space. The proposed method for a particular case is formulated and explained for quadratic functions. To reduce the number of optimization parameters, the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to solve. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method. Originality/value This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel functions. This work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.

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Section: Constraint-based Methods In Constrained Clusteringmentioning

confidence: 99%

Clustering in the presence of side information: a non-linear approach

Abin

2019

IJICC

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“…The most common way of using pairwise constraints in SSC relies on modifying the underlying cost function of a classical unsupervised clustering models [14,9,15].…”

Section: Related Workmentioning

confidence: 99%

A classification-based approach to semi-supervised clustering with pairwise constraints

2020

Self Cite

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In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages: the first stage uses a pair of Siamese neural networks to label the unlabeled pairs of points as must-link or cannot-link; the second stage uses the fully pairwise-labeled dataset produced by the first stage in a supervised neural-network-based clustering method.The proposed approach, S 3 C 2 (Semi-Supervised Siamese Classifiers for Clustering), is motivated by the observation that binary classification (such as assigning pairwise relations) is usually easier than multi-class clustering with partial supervision. On the other hand, being classification-based, our method solves only well-defined classification problems, rather than less well specified clustering tasks. Extensive experiments on various datasets demonstrate the high performance of the proposed method.

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“…Shental et al [29] constructed a version of Gaussian mixture model, which gathers data points into equivalence classes (called chunklets) using must-link relation and then applied EM algorithm on such generalized data set of chunklets. This approach was later modified to multi-modal clustering models [32]. The aforementioned methods work well with noiseless side information, but deteriorate the results when some constraints are mislabeled.…”

Section: Related Workmentioning

confidence: 99%