Multi-View Clustering

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

With a plethora of data capturing modalities becoming available, the same data object often leaves different kinds of digital footprints. This naturally leads to datasets comprising the same set of data objects represented in different forms, called multi-view data. Among the most fundamental tasks in unsupervised learning is that of clustering, the task of grouping data objects into groups of related objects. Multi-view clustering (MVC) is a flourishing field in unsupervised learning; the MVC task considers leveraging multiple views of data objects in order to arrive at a more effective and accurate grouping than what can be achieved by just using one view of data. Multi-view clustering methods differ in the kind of modelling they use in order to fuse multiple views, by managing the synergies, complimentarities, and conflicts across data views, and arriving at a single clustering output across the multiple views in the dataset. This chapter provides a survey of a sample of multi-view clustering methods, with an emphasis on bringing out the wide diversity in solution formulations that have been considered. We pay specific attention to enable the reader understand the intuition behind each method ahead of describing the technical details of the method, to ensure that the survey is accessible to readers who may not be machine learning specialists. We also outline some popular datasets that have been used to empirically evaluate MVC methods.
Original languageEnglish
Title of host publicationLinking and Mining Heterogeneous and Multi-view Data
EditorsDeepak P, Anna Jurek-Loughrey
PublisherSpringer
Pages27-53
ISBN (Electronic)978-3-030-01872-6
ISBN (Print)978-3-030-01871-9
DOIs
Publication statusEarly online date - Nov 2018

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    Padmanabhan, D., & Jurek-Loughrey, A. (2018). Multi-View Clustering. In D. P, & A. Jurek-Loughrey (Eds.), Linking and Mining Heterogeneous and Multi-view Data (pp. 27-53). Springer. https://doi.org/10.1007/978-3-030-01872-6_2