Algorithms for clustering data
Algorithms for clustering data
Information retrieval
Building the data warehouse (2nd ed.)
Building the data warehouse (2nd ed.)
ACM Computing Surveys (CSUR)
A UML profile for multidimensional modeling in data warehouses
Data & Knowledge Engineering - Special issue: ER 2003
Conceptual modeling for classification mining in data warehouses
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Extending the UML for designing association rule mining models for data warehouses
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Model-Driven Metadata for OLAP Cubes from the Conceptual Modelling of Data Warehouses
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Toward data mining engineering: A software engineering approach
Information Systems
A UML profile for the conceptual modelling of data-mining with time-series in data warehouses
Information and Software Technology
A data mining approach to knowledge discovery from multidimensional cube structures
Knowledge-Based Systems
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Data guided approach to generate multi-dimensional schema for targeted knowledge discovery
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Clustering can be considered the most important unsupervised learning technique finding similar behaviors (clusters) on large collections of data. Data warehouses (DWs) can help users to analyze stored data, because they contain preprocessed data for analysis purposes. Furthermore, the multidimensional (MD) model of DWs, intuitively represents the system underneath. However, most of the clustering data mining are applied at a low-level of abstraction to complex unstructured data. While there are several approaches for clustering on DWs, there is still not a conceptual model for clustering that facilitates modeling with this technique on the multidimensional (MD) model of a DW. Here, we propose (i) a conceptual model for clustering that helps focusing on the data-mining process at the adequate abstraction level and (ii) an extension of the unified modeling language (UML) by means of the UML profiling mechanism allowing us to design clustering data-mining models on top of the MD model of a DW. This will allow us to avoid the duplication of the time-consuming preprocessing stage and simplify the clustering design on top of DWs improving the discovery of knowledge.