Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Foundations of aggregation constraints
Theoretical Computer Science
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
DMajor—Application Programming Interface for Database Mining
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Materialized Views Selection in a Multidimensional Database
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Improved Algorithm for Quantifier Elimination Over Real Closed Fields
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Constrained Gradients in Large Databases
IEEE Transactions on Knowledge and Data Engineering
A new OLAP aggregation based on the AHC technique
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mining condensed frequent-pattern bases
Knowledge and Information Systems
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach
IEEE Transactions on Knowledge and Data Engineering
Star-cubing: computing iceberg cubes by top-down and bottom-up integration
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A probabilistic model for data cube compression and query approximation
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Mining approximate top-k subspace anomalies in multi-dimensional time-series data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Scientific contributions of Leo Khachiyan (a short overview)
Discrete Applied Mathematics
Upper Borders for Emerging Cubes
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
What Can Formal Concept Analysis Do for Data Warehouses?
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Mining significant change patterns in multidimensional spaces
International Journal of Business Intelligence and Data Mining
Mining convergent and divergent sequences in multidimensional data
International Journal of Business Intelligence and Data Mining
Embedded indicators to facilitate the exploration of a data cube
International Journal of Business Intelligence and Data Mining
Extracting semantics in OLAP databases using emerging cubes
Information Sciences: an International Journal
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
Towards intensional answers to OLAP queries for analytical sessions
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
Emerging cubes for trends analysis in OLAP databases
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Mining top-K multidimensional gradients
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
| Hi-index | 0.00 |
Cubegrades are a generalization of association rules which represent how a set of measures (aggregates) is affected by modifying a cube through specialization (rolldown), generalization (rollup) and mutation (which is a change in one of the cube's dimensions). Cubegrades are significantly more expressive than association rules in capturing trends and patterns in data because they can use other standard aggregate measures, in addition to COUNT. Cubegrades are atoms which can support sophisticated “what if” analysis tasks dealing with behavior of arbitrary aggregates over different database segments. As such, cubegrades can be useful in marketing, sales analysis, and other typical data mining applications in business.In this paper we introduce the concept of cubegrades. We define them and give examples of their usage. We then describe in detail an important task for computing cubegrades: generation of significant cubes which is analogous to generating frequent sets. A novel Grid Based Pruning (GBP) method is employed for this purpose. We experimentally demonstrate the practicality of the method. We conclude with a number of open questions and possible extensions of the work.