You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

Concise case indexing of time series in health care by means of key sequence discovery


Publication Type:

Journal article


Applied Intelligence


Coping with time series cases is becoming an important issue in applications of case based reasoning in medical cares. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are nonredundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly valuable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Four alternative ways to develop case indexes based on key sequences are suggested and discussed in detail. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval. Preliminary experiment results have revealed that such case indexes utilizing key sequence information result in substantial performance improvement for the underlying case-based reasoning system.


author = {Ning Xiong and Peter Funk},
title = {Concise case indexing of time series in health care by means of key sequence discovery},
volume = {28},
pages = {247--260},
month = {June},
year = {2008},
journal = {Applied Intelligence},
url = {}