Minimum description length based hidden Markov model clustering for life sequence analysis

By Jouni Helske, Mervi Eerola and Ioan Tabus in Hidden Markov models Clustering

January 1, 2010

PDF

Abstract

In this article, a model-based method for clustering life sequences is suggested. In the social sciences, model-free clustering methods are often used in order to find typical life sequences. The suggested method, which is based on hidden Markov models, provides principled probabilistic ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and algorithm, the method is tested with real life data, where it finds eight descriptive clusters with clear probabilistic structures.

Posted on:
January 1, 2010
Length:
1 minute read, 79 words
Categories:
Hidden Markov models Clustering
Tags:
Life course data
See Also:
From sequences to variables - Rethinking the relationship between sequences and outcomes
Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
Analysing Complex Life Sequence Data with Hidden Markov Modelling