Vine copulas for mixed data : multi-view clustering for mixed data beyond meta-Gaussian dependencies

Tekumalla, Lavanya Sita; Rajan, Vaibhav; Bhattacharyya, Chiranjib

doi:10.1007/s10994-016-5624-2

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…Their method outperforms other clustering algorithms on a variety of datasets. Tekumalla et al [60] use the concept of vines copulas 2 for mixed data clustering, they propose an inferencing algorithm to fit those vines on the mixed data. A dependency-seeking multi-view clustering that uses a Dirichlet process mixture of vines is developed [60].…”

Section: Model-based Clusteringmentioning

confidence: 99%

“…Tekumalla et al [60] use the concept of vines copulas 2 for mixed data clustering, they propose an inferencing algorithm to fit those vines on the mixed data. A dependency-seeking multi-view clustering that uses a Dirichlet process mixture of vines is developed [60]. Marbac et al [61] present a mixture model of Gaussian copulas for mixed data clustering.…”

Section: Model-based Clusteringmentioning

confidence: 99%

“…Vine copulas provide a flexible way of pair-wise dependency modeling using hierarchical collections of bivariate copulas, each of which can belong to any copula family thereby capturing a wide variety of dependencies[60].…”

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confidence: 99%

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Survey of State-of-the-Art Mixed Data Clustering Algorithms

2019

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Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field. INDEX TERMS Categorical features, clustering, mixed datasets, numeric features. I. INTRODUCTION Clustering is an unsupervised machine learning technique used to group unlabeled data into clusters that contain data points that are 'similar' to each other and 'dissimilar' from those in other clusters [1], [2]. Many clustering algorithms can only handle data that contain either numeric or categorical feature values [3], [4]. Numeric features can take real values, such as height, weight, and distance. Categorical features represent data that can be divided into a fixed number of categories, such as color, race, sex, profession, and blood group. Clustering algorithms group data points into clusters using some notion of 'similarity', which can be as simple as the Euclidean distance. To compute the similarity between numeric feature values, mathematical operations (such as distances, angles, summation, or mean) are applied to them. Distance-based similarity measures are mostly used for numeric data points. Generally, categorical feature values are not inherently ordered (for example, the categorical values, red and blue). It is not possible to directly compute the distance between two categorical feature values. Therefore, computing distance-based similarity measures for categorical data is a challenging task [5]. Nevertheless, several methods The associate editor coordinating the review of this manuscript and approving it for publication was Haruna Chiroma.

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Section: Model-based Clusteringmentioning

confidence: 99%

Section: Model-based Clusteringmentioning

confidence: 99%

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Survey of State-of-the-Art Mixed Data Clustering Algorithms

2019

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“…[138]. 2 Vine copulas provide a flexible way of pair-wise dependency modeling using hierarchical collections of bivariate copulas, each of which can belong to any copula family thereby capturing a wide variety of dependencies [60]. multinomial mixture models [135].…”

Section: Incremental Clusteringmentioning

confidence: 99%

“…Rajan and Bhattacharya [59] Gaussian mixture copula. Tekumalla1 et al [60] Vine copulas and Dirichlet process mixture of vines. Marbac [61] A mixture model of Gaussian copulas.…”

Section: Mcparland and Gormley [56]mentioning

confidence: 99%

Survey of state-of-the-art mixed data clustering algorithms

Ahmad,

Khan

2018

Preprint

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Representation Learning in Multi-view Clustering: A Literature Review

Chen

Lin

et al. 2022

Data Sci. Eng.

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Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.

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Vine copulas for mixed data : multi-view clustering for mixed data beyond meta-Gaussian dependencies

Cited by 10 publications

References 34 publications

Survey of State-of-the-Art Mixed Data Clustering Algorithms

Survey of State-of-the-Art Mixed Data Clustering Algorithms

Survey of state-of-the-art mixed data clustering algorithms

Representation Learning in Multi-view Clustering: A Literature Review

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