Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Algorithms
Generalized Decision Algorithms, Rough Inference Rules, and Flow Graphs
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Rough Set Flow Graphs and Max - * Fuzzy Relation Equations in State Prediction Problems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Knowledge discovery by rough sets mathematical flow graphs and its extension
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Interpretation of extended Pawlak flow graphs using granular computing
Transactions on rough sets VIII
An extension of pawlak's flow graphs
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Decision trees and flow graphs
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Transactions on Rough Sets III
An efficient algorithm for inference in rough set flow graphs
Transactions on Rough Sets V
Flow graphs and decision tables with fuzzy attributes
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Pawlak's flow graphs have attracted both practical and theoretical researchers because of their ability to visualize information flow. In this paper, we invent a new schema to represent throughflow of a flow graph and three coefficients of both normalized and combined normalized flow graphs in matrix form. Alternatively, starting from a flow graph with its throughflow matrix, we reform Pawlak's formulas to calculate these three coefficients in flow graphs by using matrix properties. While traditional algorithms for computing these three coefficients of the connection are exponential in l, an algorithm using our matrix representation is polynomial in l, where l is the number of layers of a flow graph. The matrix form can simplify computation, improve time complexity, alleviate problems due to missing coefficients and hence help to widen the applications of flow graphs. Practically, data sets often reside at different sources (heterogeneous data sources). Their individual analysis at each source is inadequate and requires special treatment. Hence, we introduce a composition method for flow graphs and corresponding formulas for calculating their coefficients which can omit some data sharing. We provide a real-world experiment on the Promotion of Academic Olympiads and Development of Science Education Foundation (POSN) data set which illustrates a desirable outcome and the advantages of the proposed matrix forms and the composition method.