## Transactions on Rough Sets VI: Commemorating Life and Work of Zdislaw Pawlak, Volume 6 (Google eBook)James F. Peters, Ivo Düntsch, Jerzy Grzymala-Busse, Ewa Orlowska, Lech Polkowski Springer Science & Business Media, 2007 - 498 pages The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume VI of the Transactions on Rough Sets (TRS) commemorates the life and work of Zdzislaw Pawlak (1926-2006). His legacy is rich and varied. Prof. Pawlak's research contributions have had far-reaching implications inasmuch as his works are fundamental in establishing new perspectives for scientific research in a wide spectrum of fields. This volume of the TRS presents papers that reflect the profound influence of a number of research initiatives by Professor Pawlak. In particular, this volume introduces a number of new advances in the foundations and applications of artificial intelligence, engineering, logic, mathematics, and science. These advances have significant implications in a number of research areas such as the foundations of rough sets, approximate reasoning, bioinformatics, computational intelligence, cognitive science, data mining, information systems, intelligent systems, machine intelligence, and security. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

1 | |

Intuitionistic Rough Sets for Database Applications | 26 |

An Experimental Comparison of Three Rough Set Approaches to Missing Attribute Values | 31 |

Pawlaks Landscaping with Rough Sets | 51 |

A Comparison of Pawlaks and SkowronStepaniuks Approximation of Concepts | 64 |

Data Preparation for Data Mining in Medical Data Sets | 83 |

A Wistech Paradigm for Intelligent Systems | 94 |

The Domain of Acoustics Seen from the Rough Sets Perspective | 133 |

On Partial Covers Reducts and Decision Rules with Weights | 211 |

A Personal View on AI Rough Set Theory and Professor Pawlak | 247 |

A Proposition on How to Learn Concepts in Humane Sciences by Means of Rough Set Theory | 298 |

Discovering Association Rules in Incomplete Transactional Databases | 308 |

On Combined Classifiers Rule Induction and Rough Sets | 329 |

Approximation Spaces in Multi Relational Knowledge Discovery | 351 |

A Distributed Computing Hybrid Data Mining Strategy | 366 |

A Model PM for Preprocessing and Data Mining Proper Process | 397 |

Extension and a Case Study | 152 |

In Commemoration of Professor Zdzislaw Pawlak | 172 |

On Representation and Analysis of Crispand Fuzzy Information Systems | 191 |

Author Index | 499 |

### Common terms and phrases

Alexandrov Alexandrov topology algebra applied approach approximation mappings approximation spaces association rules binary relation Boolean lattice classiﬁers closure operator complete join-morphism complete lattice Computer Science concept condition attributes consider constructed DARIT Data Mining data set database decision attribute decision rules decision table deﬁned deﬁnition diﬀerent domain eﬀects elements equivalence relation evaluation example ﬁeld ﬁnd ﬁnite ﬁrst fixpoint fuzzy rough fuzzy set Galois connection Granular Computing greedy algorithm Hence implies included incomplete indiscernibility relation induced information granules information system intuitionistic itemset Knowledge Discovery learning Lemma Let us denote logic lower approximation minimal missing attribute values MODLEM noise objects obtained ordered set P-system pair Pawlak Polkowski Proof properties Proposition real numbers reducts reﬂexive rough inclusion rough set theory Section set cover problem Skowron speciﬁc Springer-Verlag structure subset Theorem tion topological Transactions on Rough upper approximation uppS weight function wisdom wistech