This paper presents an inter-class pattern discovery method in real world database. While data in conventional database has tuple structure, the data in pattern discovery has set-values or sequences. The structural difference between them may cause useless resulting patterns and may result in inefficient pattern discovery method. To resolve those issues, we propose an inter-class pattern discovery methodology. The first step is to convert conventional database to set of objects. During the conversion process, a tuple in the original database is converted to a conceptual object and as another result, object generalization hierarchies are generated. From the object generalization hierarchies, interesting patterns of the conceptual objects can be extracted by applying multi-level pattern discovery algorithms. The resulting patterns are inter-class patterns of original conventional database. We also show a pattern discovery query for our methodology and its application on intelligent query processing.
|Number of pages||6|
|Publication status||Published - 1997|
|Event||Proceedings of the 1997 8th International Workshop on Database and Expert Systems Applications, DEXA'97 - Toulouse, Fr|
Duration: 1997 Sept 1 → 1997 Sept 2
|Conference||Proceedings of the 1997 8th International Workshop on Database and Expert Systems Applications, DEXA'97|
|Period||97/9/1 → 97/9/2|
All Science Journal Classification (ASJC) codes