Computational geometry: an introduction
Computational geometry: an introduction
Computing the largest empty rectangle
SIAM Journal on Computing
Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An Integrated Framework for Empirical Discovery
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Mining interesting knowledge using DM-II
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining for Empty Rectangles in Large Data Sets
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Efficient Mining of Niches and Set Routines
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Mining for empty spaces in large data sets
Theoretical Computer Science - Database theory
Mining dependence rules by finding largest itemset support quota
Proceedings of the 2004 ACM symposium on Applied computing
Improving GA search reliability using maximal hyper-rectangle analysis
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A rank-by-feature framework for interactive exploration of multidimensional data
Information Visualization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast algorithms for finding disjoint subsequences with extremal densities
Pattern Recognition
Discrete Applied Mathematics - Special issue: Discrete algorithms and optimization, in honor of professor Toshihide Ibaraki at his retirement from Kyoto University
Complexity-guided case discovery for case based reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
An intersection inequality for discrete distributions and related generation problems
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Minimum variance associations: discovering relationships in numerical data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Fast algorithms for finding disjoint subsequences with extremal densities
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
MFCS'05 Proceedings of the 30th international conference on Mathematical Foundations of Computer Science
Localized geometric query problems
Computational Geometry: Theory and Applications
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Current machine learning and discovery techniques focus on discovering rules or regularities that exist in data. An important aspect of the research that has been ignored in the past is the learning or discovering of interesting holes in the database. If we view each case in the database as a point in a it-dimensional space, then a hole is simply a region in the space that contains no data point. Clearly, not every hole is interesting. Some holes are obvious because it is known that certain value combinations are not possible. Some holes exist because there are insufficient cases in the database. However, in some situations, empty regions do carry important information. For instance, they could warn us about some missing value combinations that are either not known before or are unexpected. Knowing these missing value combinations may lead to significant discoveries. In this paper, we propose an algorithm to discover holes in databases.