TY - GEN

T1 - Scheduled discovery of exception rules

AU - Suzuki, Einoshin

PY - 1999/1/1

Y1 - 1999/1/1

N2 - This paper presents an algorithm for discovering pairs of an exception rule and a common sense rule under a prespecified schedule. An exception rule, which represents a regularity of exceptions to a common sense rule, often exhibits interestingness. Discovery of pairs of an exception rule and a common sense rule has been successful in various domains. In this method, however, both the number of discovered rules and time needed for discovery depend on the values of thresholds, and an appropriate choice of them requires expertise on the data set and on the discovery algorithm. In order to circumvent this problem, we propose two scheduling policies for updating values of these thresholds based on a novel data structure. The data structure consists of multiple balanced search-trees, and efficiently manages discovered patterns with multiple indices. One of the policies represents a full specification of updating by the user, and the other iteratively improves a threshold value by deleting the worst pattern with respect to its corresponding index. Preliminary results on four real-world data sets are highly promising. Our algorithm settled values of thresholds appropriately, and discovered interesting exception-rules from all these data sets.

AB - This paper presents an algorithm for discovering pairs of an exception rule and a common sense rule under a prespecified schedule. An exception rule, which represents a regularity of exceptions to a common sense rule, often exhibits interestingness. Discovery of pairs of an exception rule and a common sense rule has been successful in various domains. In this method, however, both the number of discovered rules and time needed for discovery depend on the values of thresholds, and an appropriate choice of them requires expertise on the data set and on the discovery algorithm. In order to circumvent this problem, we propose two scheduling policies for updating values of these thresholds based on a novel data structure. The data structure consists of multiple balanced search-trees, and efficiently manages discovered patterns with multiple indices. One of the policies represents a full specification of updating by the user, and the other iteratively improves a threshold value by deleting the worst pattern with respect to its corresponding index. Preliminary results on four real-world data sets are highly promising. Our algorithm settled values of thresholds appropriately, and discovered interesting exception-rules from all these data sets.

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U2 - 10.1007/3-540-46846-3_17

DO - 10.1007/3-540-46846-3_17

M3 - Conference contribution

AN - SCOPUS:84957811911

SN - 354066713X

SN - 9783540667131

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 184

EP - 195

BT - Discovery Science - 2nd International Conference, DS 1999, Proceedings

A2 - Arikawa, Setsuo

A2 - Furukawa, Koichi

PB - Springer Verlag

T2 - 2nd International Conference on Discovery Science, DS 1999

Y2 - 5 December 1999 through 7 December 1999

ER -