Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Computing appropriate representations for multidimensional data
Data & Knowledge Engineering - Special issue: Advances in OLAP
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A note on quality measures for fuzzy association rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Pixelizing data cubes: a block-based approach
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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Multidimensional databases are now recognized as being the standard way to store aggregated and historized data. Multidimensional databases are designed to store information on measures (also known as indicators) regarding a set of dimensions. One important issue in this framework is the identification of homogeneous areas in data cubes, which allows users to summarize and visualize the data through the main trends they contain. In our previous work, we have proposed a levelwise approach to mine homogeneous areas of the data, called blocks that can be interpreted, for instance, as If product is Chocolate and month is between January and March and city is London or Paris, then the number of sales is 5. However, in this work, the information provided by the hierarchies defined over the dimensions is not taken into account. In this paper, we consider the case where measure values are discretized using a fuzzy partition, and we extend our method so as to mine multiple-level fuzzy blocks, that is, blocks that are defined using hierarchies and that characterize fuzzy measure values. Moreover, in order to avoid redundancies in the output set of blocks, only the most specific ones (according to hierarchies) are computed. We show that our algorithms are linear in the size of the cube, thus providing an efficient method for summarizing data cubes.