Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation

TL;DR

This paper introduces extsc{ciif}, an interactive clustering method that allows users to reject and request new clusters, formalized through a Bayesian prior elicitation framework, enabling efficient and diverse data exploration.

Contribution

It presents a novel Bayesian framework for interactive clustering that incorporates user feedback through cluster rejection, improving clustering diversity and relevance.

Findings

extsc{ciif} produces accurate clusterings.

It generates diverse clustering solutions.

The method is computationally efficient for interactive use.

Abstract

A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criteria. These criteria can be difficult to formalize, even when it is easy for an analyst to know a good clustering when she sees one. We present a new approach to interactive clustering for data exploration, called \ciif, based on a particularly simple feedback mechanism, in which an analyst can choose to reject individual clusters and request new ones. The new clusters should be different from previously rejected clusters while still fitting the data well. We formalize this interaction in a novel Bayesian prior elicitation framework. In each iteration, the prior is adapted to account for all the previous feedback, and a new clustering is then produced from the posterior distribution. To achieve the computational efficiency necessary for an interactive setting, we propose an incremental optimization method over data minibatches using Lagrangian relaxation. Experiments demonstrate that \ciif can produce accurate and diverse clusterings.