# Automated Exploratory Clustering to Democratize Clustering Analysis
## 1 Introduction  
AutoML [1, 2, 3] democratizes the field of machine learning (ML) by enabling low-to-moderate experienced practitioners and researchers to find suitable models for individual scenarios and use cases. The search for the most performant algorithm including its hyperparameters has been formalized as the CASH problem in [4] and since been used in many frameworks like AutoGluon-Tabular [5] or TSC-AutoML [6]. Different algorithms and models are trialled and evaluated based on specific performance metrics such as loss or accuracy in a supervised setting. By making use of AutoML approaches, experts as well as beginners in the field of machine learning are able to find apt models, including neural networks [7] or specialized models for time series classification [8] and multimodal data [9]. In this way, finding a suitable ML model is possible for anyone having access to sufficient computing power, not just for ML experts. While AutoML approaches are typically wellsuited for supervised learning, their application becomes more intricate in the domain of unsupervised learning. The field of clustering [10] is a widely known and frequently encountered area in unsupervised learning. While many different clustering methods [11; 12; 13; 14] have been proposed in recent years, this field is still notorious for its ambiguous results, as no particular clustering can ever satisfy the needs of all practitioners [15]. This leads to the challenge of automatically determining a \"good\" clustering for a particular problem, as a useful clustering for one practitioner might be useless for someone else having a different notion of interestingness. As a direct consequence of this, there can be no single measure to find the best clustering for a given dataset. As an example, let us consider the context of Exploratory Data Analysis [16], where it is important to gain insights in multiple views of the data to derive meaningful hypotheses, as opposed to the process of using clustering as a preprocessing step, where a single clustering might be beneficiary. This subjective nature of clustering makes it necessary to not present a single best clustering, as in current Automated Clustering approaches [17; 18; 19; 20], but to deliver a specific selection of clusterings representing different, possibly \"good\" clusterings [21; 22] for further exploration. We denote the goal of finding a selection of clusterings most likely containing a highly interesting clustering for the user as _Automated Exploratory Clustering_ (AEC). Hence, a central task in AEC is not only to provide \"good\" (meaning interesting) clusterings to a user, but to also balance the tradeoff between _coverage_, i.e., showing every interesting clustering to a user, and _minimality_, i.e., keeping the number of shown clusterings as low as possible. This balance will prevent the user from being overwhelmed from the amount of delivered clustering solutions and help them or her to understand the most relevant ones. An exemplary overview of the Automated Exploratory Clustering problem can be seen in Figure 1.  
In this paper, we introduce a formal definition of the AEC problem and provide empirical evidence for its significance. We show, that AEC unifies current AutoML approaches for clustering [23], which mainly focus on determining a single high-quality clustering, thus optimizing selectiveness. We also show that the approach in [24] is, contrarily to this balance, maximizing the number of interesting clusterings (coverage) at the risk of including unnecessary clusterings. We will also propose an efficient method to trade off between these approaches with comparatively performant results. In this method, we  
Figure 1: Problem of Automated Exploratory Clustering (AEC). A user has a dataset of different objects. Using AEC, they retrieve two different interesting clusterings.