C4.5: programs for machine learning
C4.5: programs for machine learning
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast density estimation using CF-kernel for very large databases
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Journal of Intelligent Information Systems
Machine Learning
Machine Learning
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
NetCube: A Scalable Tool for Fast Data Mining and Compression
Proceedings of the 27th International Conference on Very Large Data Bases
Nonparametric estimation of distributions with categorical and continuous data
Journal of Multivariate Analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Answering top-k queries with multi-dimensional selections: the ranking cube approach
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Bellwether analysis: predicting global aggregates from local regions
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Research in data warehouse modeling and design: dead or alive?
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Sampling cube: a framework for statistical olap over sampling data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Workload-aware anonymization techniques for large-scale datasets
ACM Transactions on Database Systems (TODS)
Adversarial-knowledge dimensions in data privacy
The VLDB Journal — The International Journal on Very Large Data Bases
Splash: ad-hoc querying of data and statistical models
Proceedings of the 13th International Conference on Extending Database Technology
Latent OLAP: data cubes over latent variables
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
A neural-based approach for extending OLAP to prediction
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Discovering frequent pattern pairs
Intelligent Data Analysis
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In this paper, we introduce a new family of tools for exploratory data analysis, called prediction cubes. As in standard OLAP data cubes, each cell in a prediction cube contains a value that summarizes the data belonging to that cell, and the granularity of cells can be changed via operations such as roll-up and drill-down. In contrast to data cubes, in which each cell value is computed by an aggregate function, e.g., SUM or AVG, each cell value in a prediction cube summarizes a predictive model trained on the data corresponding to that cell, and characterizes its decision behavior or predictiveness. In this paper, we propose and motivate prediction cubes, and show that they can be efficiently computed by exploiting the idea of model decomposition.