C4.5: programs for machine learning
C4.5: programs for machine learning
From optimal hyperplanes to optimal decision trees
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
Efficient SQL-Querying Method for Data Mining in Large Data Bases
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Boolean Reasoning for Feature Extraction Problems
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Fast split selection method and its application in decision tree construction from large databases
International Journal of Hybrid Intelligent Systems - Hybrid Intelligence using rough sets
Information Sciences: an International Journal
Similarity Relation in Classification Problems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Rule-Based Similarity for Classification
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Discovering rules-based similarity in microarray data
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Applications of approximate reducts to the feature selection problem
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A new method for discretization of continuous attributes based on VPRS
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Approximate boolean reasoning approach to rough sets and data mining
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Time complexity of decision trees
Transactions on Rough Sets III
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Dynamic rule-based similarity model for DNA microarray data
Transactions on Rough Sets XV
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Some data mining techniques, like discretization of continuous attributes or decision tree induction, are based on searching for an optimal partition of data with respect to some optimization criteria. We investigate the problem of searching for optimal binary partition of continuous attribute domain in case of large data sets stored in relational data bases (RDB). The critical for time complexity of algorithms solving this problem is the number of I/O database operations necessary to construct such partitions. In our approach the basic operators are defined by queries on the number of objects characterized by means of real value intervals of continuous attributes. We assume the answer time for such queries does not depend on the interval length. The straightforward approach to the optimal partition selection (with respect to a given measure) requires O(N) basic queries, where N is the number of preassumed partition parts in the searching space. We show properties of the basic optimization measures making possible to reduce the size of searching space. Moreover, we prove that using only O(log N) simple queries, one can construct a partition very close to optimal.