Clustering Algorithms for ITS Sequence Data with Alignment Metrics
Kelarev, Andrei and Kang, Byeong Ho and Steane, Dorothy (2006) Clustering Algorithms for ITS Sequence Data with Alignment Metrics. In: The 19th Australian Joint Conference on Artificial Intelligence, 4-8 Dec 2003, Hobart, Australia. Preview |
| PDF - Requires a PDF viewer 126Kb |
AbstractReal-world document classification is an open-ended problem, rather than a close-ended problem, because the document classification domain continually evolves as the time passes. Unlike the close-ended document classification, the participants in the open-ended problem actively take part in the problem solving process. For this reason, it is important to understand the problem solver's behavioral characteristics. This paper proposes a thorough analysis of them. We found that the problem solving strategies are significantly different among participants because of individual differences in cognition among participants
Repository Staff Only: item control page
|