  Existing task-oriented chatbots heavily rely on spoken language understanding
(SLU) systems to determine a user's utterance's intent and other key
information for fulfilling specific tasks. In real-life applications, it is
crucial to occasionally induce novel dialog intents from the conversation logs
to improve the user experience. In this paper, we propose the Density-based
Deep Clustering Ensemble (DDCE) method for dialog intent induction. Compared to
existing K-means based methods, our proposed method is more effective in
dealing with real-life scenarios where a large number of outliers exist. To
maximize data utilization, we jointly optimize texts' representations and the
hyperparameters of the clustering algorithm. In addition, we design an
outlier-aware clustering ensemble framework to handle the overfitting issue.
Experimental results over seven datasets show that our proposed method
significantly outperforms other state-of-the-art baselines.
