  The overwhelming presence of categorical/sequential data in diverse domains
emphasizes the importance of sequence mining. The challenging nature of
sequences proves the need for continuing research to find a more accurate and
faster approach providing a better understanding of their (dis)similarities.
This paper proposes a new Model-based approach for clustering sequence data,
namely nTreeClus. The proposed method deploys Tree-based Learners, k-mers, and
autoregressive models for categorical time series, culminating with a novel
numerical representation of the categorical sequences. Adopting this new
representation, we cluster sequences, considering the inherent patterns in
categorical time series. Accordingly, the model showed robustness to its
parameter. Under different simulated scenarios, nTreeClus improved the baseline
methods for various internal and external cluster validation metrics for up to
10.7% and 2.7%, respectively. The empirical evaluation using synthetic and real
datasets, protein sequences, and categorical time series showed that nTreeClus
is competitive or superior to most state-of-the-art algorithms.
