DocCube: multi-dimensional visualisation and exploration of large document sets
Journal of the American Society for Information Science and Technology
A new OLAP aggregation based on the AHC technique
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Multidimensional models: constructing data CUBE
CompSysTech '04 Proceedings of the 5th international conference on Computer systems and technologies
Knowledge Mining for the Business Analyst
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
A search space reduction methodology for data mining in large databases
Engineering Applications of Artificial Intelligence
A search space reduction methodology for large databases: a case study
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Algorithm using hypercube for aggregations
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
The computation of semantic data cube
GCC'05 Proceedings of the 4th international conference on Grid and Cooperative Computing
A knowledge mining framework for business analysts
ACM SIGMIS Database
An automated search space reduction methodology for large databases
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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As the size of data warehouses increase to several hundreds of gigabytes or terabytes, the need for methods and tools that will automate the process of knowledge extraction, or guide the user to subsets of the dataset that are of particular interest, is becoming prominent. In this survey paper we explore the problem of identifying and extracting interesting knowledge from large collections of data residing in data warehouses, by using data mining techniques. Such techniques have the ability to identify patterns and build succinct models to describe the data. These models can also be used to achieve summarization and approximation. We review the associated work in the OLAP, data mining, and approximate query answering literature. We discuss the need for the traditional data mining techniques to adapt, and accommodate the specific characteristics of OLAP systems. We also examine the notion of interestingness of data, as a tool to guide the analysis process. We describe methods that have been proposed in the literature for determining what is interesting to the user and what is not, and how these approaches can be incorporated in the data mining algorithms.