Abstract
To provide a parsimonious generative representation of the sequential activity of a number of individuals within a population there is a necessary tradeoff between the definition of individual specific and global representations. A linear-time algorithm is proposed that defines a distributed predictive model for finite state symbolic sequences which represent the traces of the activity of a number of individuals within a group. The algorithm is based on a straightforward generalization of latent Dirichlet allocation to time-invariant Markov chains of arbitrary order. The modelling assumption made is that the possibly heterogeneous behavior of individuals may be represented by a relatively small number of simple and common behavioral traits which may interleave randomly according to an individual-specific distribution. The results of an empirical study on three different application domains indicate that this modelling approach provides an efficient low-complexity and intuitively interpretable representation scheme which is reflected by improved prediction performance over comparable models.
Original language | English |
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Pages (from-to) | 175-196 |
Number of pages | 22 |
Journal | Data Mining and Knowledge Discovery |
Volume | 10 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 May 2005 |
Keywords
- Markov chains
- mixture models
- user profiling