Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Learning of Simple Conceptual Graphs from Positive and Negative Examples
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
A local approach to concept generation
Annals of Mathematics and Artificial Intelligence
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Discovery of optimal factors in binary data via a novel method of matrix decomposition
Journal of Computer and System Sciences
Parallel algorithm for computing fixpoints of Galois connections
Annals of Mathematics and Artificial Intelligence
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A parallel algorithm for lattice construction
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Distributed formal concept analysis algorithms based on an iterative mapreduce framework
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
CA-ABAC: Class Algebra Attribute-Based Access Control
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Searching for interesting patterns in binary matrices plays an important role in data mining and, in particular, in formal concept analysis and related disciplines. Several algorithms for computing particular patterns represented by maximal rectangles in binary matrices were proposed but their major drawback is their computational complexity limiting their application on relatively small datasets. In this paper we introduce a scalable distributed algorithm for computing maximal rectangles that uses the map-reduce approach to data processing.