A generative probabilistic approach to visualizing sets of symbolic sequences

There is a notable interest in extending probabilistic generative modeling principles to accommodate for more complex structured data types. In this paper we develop a generative probabilistic model for visualizing sets of discrete symbolic sequences. The model, a constrained mixture of discrete hidden Markov models, is a generalization of density-based visualization methods previously developed for static data sets. We illustrate our approach on sequences representing web-log data and chorals by J.S. Bach. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms: Algorithms, Design, Theory

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Peter Tiño, Ata Kabán, Yi Sun
• Keywords: Data Mining, Discrete Symbolic Sequences, Generative Probabilistic Model, KDD 2004, Probabilistic Generative Modeling