Data Mining with Calendar Attributes
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
SemGrAM: integrating semantic graphs into association rule mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Generating qualified summarization answers using fuzzy concept hierarchies
Proceedings of the 2010 Symposium on Information and Communication Technology
From data to global generalized knowledge
Decision Support Systems
Towards intensional answers to OLAP queries for analytical sessions
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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In this paper, we introduce a partially automated method for generating intensional answers at multiple abstraction levels for a query, which can help database users find more interesting and desired answers. Our approach consists of three phases: preprocessing, query execution, and answer generation. In the preprocessing phase, we build a set of concept hierarchies constructed by generalization of data stored in a database and a set of virtual hierarchies to provide a global view of relationships among high-level concepts from multiple concept hierarchies. In the query execution phase, we receive a user's query, process the query, collect an extensional answer, and select a set of relevant attributes to be generalized in the extensional answer. In the answer generation phase, we find the general characteristics of those relevant attribute values at multiple abstraction levels with the concept hierarchies and the virtual hierarchies by using data mining methods. The main contribution of this paper is that we apply and extend data mining methods to generate intensional answers at multiple abstraction levels, which increases the relevance of the answers. In addition, we suggest strategies to avoid meaningless intensional answers, which substantially reduces the computational complexity of the intensional answer generation process.