Towards on-line analytical mining in large databases
ACM SIGMOD Record
Clustering methods for large databases: from the past to the future
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Component technologies: Java beans, COM, CORBA, RMI, EJB and the CORBA component model
Proceedings of the 24th International Conference on Software Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
iDiff: Informative Summarization of Differences in Multidimensional Aggregates
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach
IEEE Transactions on Knowledge and Data Engineering
CrossClus: user-guided multi-relational clustering
Data Mining and Knowledge Discovery
An approach to optimize data processing in business processes
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
ACM Transactions on Knowledge Discovery from Data (TKDD)
OLAP over continuous domains via density-based hierarchical clustering
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Enhanced clustering of complex database objects in the clustcube framework
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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In this paper, we introduce and experimentally assess ClustCube, an innovative OLAP-based framework for clustering and mining complex database objects extracted from distributed database settings by means of complex SQL statements involving multiple JOIN queries across (distributed) relational tables. To this end, ClustCube puts together conventional clustering techniques and well-consolidated OLAP methodologies in order to achieve higher expressive power and mining effectiveness over traditional methodologies for mining tuple-oriented information. A relevant challenge in our research is represented by the issue of efficiently computing ClustCube cubes, enriched by the respective cuboid lattices, which may represent a critical bottleneck for the proposed ClustCube framework. To face-off this drawback, we propose a collection of algorithms that implement an innovative distributive approach taking advantages from both the structured nature of complex database objects within cuboids and the distributive nature of clustering across hierarchical domains, like those defined by conventional OLAP schemas.