Abstract
In this paper, we propose a method which visualizes irregular multi-dimensional time-series data as a sequence of probabilistic prototypes for detecting exceptions from medical test data. Conventional visualization methods often require iterative analysis and considerable skill thus are not totally supported by a wide range of medical experts. Our PrototypeLines displays summarized information based on a probabilistic mixture model by using hue only thus is considered to exhibit novelty. The effectiveness of the summarization is pursued mainly through use of a novel information criterion. We report our endeavor with chronic hepatitis data, especially discoveries of interesting exceptions by a non-expert and an untrained expert.
Original language | English |
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Title of host publication | Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003 |
Pages | 315-322 |
Number of pages | 8 |
Publication status | Published - Dec 1 2003 |
Externally published | Yes |
Event | 3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States Duration: Nov 19 2003 → Nov 22 2003 |
Other
Other | 3rd IEEE International Conference on Data Mining, ICDM '03 |
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Country/Territory | United States |
City | Melbourne, FL |
Period | 11/19/03 → 11/22/03 |
All Science Journal Classification (ASJC) codes
- Engineering(all)