Tekijä: James F. Peters (ed.); Andrzej Skowron (ed.); Tianrui Li (ed.); Yan Yang (ed.); JingTao Yao (ed.); Hung Son Nguyen (ed.) Kustantaja: Springer (2014) Saatavuus: Noin 17-20 arkipäivää
Tekijä: James F. Amatruda (ed.); Corinne Houart (ed.); Koichi Kawakami (ed.); Kenneth D. Poss (ed.) Kustantaja: Humana (2023) Saatavuus: Noin 17-20 arkipäivää
Tekijä: James A. Foster (ed.); Evelyne Lutton (ed.); Julian F. Miller (ed.); Conor Ryan (ed.); Andra G.B. Tezzamanzi (ed.) Kustantaja: Springer (2002) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Peter Reimer (ed.); Paul M. Parizel (ed.); James F.M. Meaney (ed.); Falko-Alexander Stichnoth (ed.) Kustantaja: Springer (2010) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Charlotte M. Witmer (ed.); Robert R. Snyder (ed.); David J. Jollow (ed.); George F. Kalf (ed.); James J. Kocsis (ed.) Kustantaja: Springer (2012) Saatavuus: Noin 17-20 arkipäivää
Springer Sivumäärä: 461 sivua Asu: Pehmeäkantinen kirja Painos: 2005 Julkaisuvuosi: 2005, 17.05.2005 (lisätietoa) Kieli: Englanti
Volume III of the Transactions on Rough Sets (TRS) introduces advances in the theory and application of rough sets. These advances have far-reaching impli- tions in a number of researchareas such as approximate reasoning, bioinform- ics,computerscience,datamining,engineering(especially,computerengineering and signal analysis), intelligent systems, knowledge discovery, pattern recog- tion,machineintelligence,andvariousformsoflearning. This volumerevealsthe vigor, breadth and depth in research either directly or indirectly related to the rough sets theory introduced by Prof. Zdzis law Pawlak more than three decades ago. Evidence of this can be found in the seminal paper on data mining by Prof. Pawlak included in this volume. In addition, there are eight papers on the theory and application of rough sets as well as a presentation of a new version of the Rough Set Exploration System (RSES) tool set and an introduction to the Rough Set Database System (RSDS). Prof. Pawlak has contributed a pioneering paper on data mining to this v- ume. In this paper, it is shown that information ?ow in a ?ow graph is governed by Bayes' rule with a deterministic rather than a probabilistic interpretation. A cardinal feature of this paper is that it is self-contained inasmuch as it not only introduces a new viewof information?owbut alsoprovidesanintroduction to the basic concepts of ?ow graphs. The representation of information ?ow - troduced in this paper makes it possible to study di?erent relationships in data and establishes a basis for a new mathematical tool for data mining. Inadditionto thepaperbyProf.
