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Artificial Intelligence Review

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08-10-2020

Interactive clustering: a scoping review

Authors: Thais Rodrigues Neubauer, Sarajane Marques Peres, Marcelo Fantinato, Xixi Lu, Hajo Alexander Reijers

Published in: Artificial Intelligence Review | Issue 4/2021

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Abstract

We present in this paper a scoping review conducted in the interactive clustering area. Interactive clustering has been applied to leverage the strengths of both unsupervised and supervised learning. In interactive clustering, supervised learning is represented by inserting the knowledge of human experts in an originally unsupervised data analysis process. This scoping review aimed to organize the knowledge on (i) the applicability of interactive clustering methods, (ii) clustering algorithms being used to support interactive clustering, (iii) how to model the expert supervision and (iv) the effects brought by the expert supervision in the results produced. A systematic search for related literature was conducted in the Scopus database, resulting in the selection of 50 primary studies published by 2018. The analysis of these studies allowed us to identify trends such as: the application in text/image; use of partitioning and hierarchical algorithms; application of strategies based on split/merge, pairwise constraints, similarity metrics learning and data reassignment; and concern with visualization. In addition, some relevant issues not yet adequately addressed were identified, such as: the evaluation of expert supervision; the evaluation of the expert’s effort; and the conduction of studies effectively involving human experts, instead of computer simulations.

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Imposing restrictions on the clustering algorithm is implemented following the constrained clustering principles (Han et al. 2011; Chapelle et al. 2010). According to Wagstaff (2010), constrained clustering algorithms may enforce every constraint in the solution, or they may use the constraints as guidance rather than hard requirements.

The term active learning is used when the learner has some role in determining on what data it will be trained (Cohn 2010), which can be done based on expert’s knowledge.

https://www.elsevier.com/solutions/scopus/how-scopus-works/content.

For 2018, only publications indexed until September were analyzed.

The reproducibility evaluation disregarded the need for a sample of experts statistically equivalent to that of the study carried out.

As there are studies that present experiments with different data types, the sum accumulated in the graph is higher than the number of studies analyzed.

independent and identically distributed (i.i.d.)—from the probability theory and statistics.

Only acquisition strategies found in at least two primary studies are shown in the figure.

As there are studies that present experiments with different strategies for acquiring knowledge, the sum accumulated in the graph is higher than the number of studies analyzed.

Only strategies found in at least two primary studies are shown in the figure.

As there are studies that present experiments with different strategies for using the acquired knowledge, the sum accumulated in the graph is higher than the number of studies analyzed.

As there are studies that present experiments using different evaluation strategies, the sum accumulated in the graph is higher than the number of studies analyzed.

Only validation measures found in at least two primary studies are shown in the figure.

As there are studies that present experiments using different validation measures, the sum accumulated in the graph is higher than the number of studies analyzed.

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Title: Interactive clustering: a scoping review
Authors: Thais Rodrigues Neubauer
Sarajane Marques Peres
Marcelo Fantinato
Xixi Lu
Hajo Alexander Reijers
Publication date: 08-10-2020
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 4/2021
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-020-09913-7

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