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JournalISSN: 0020-0255

Information Sciences

About: Information Sciences is an academic journal. The journal publishes majorly in the area(s): Fuzzy logic & Fuzzy number. It has an ISSN identifier of 0020-0255. Over the lifetime, 13490 publication(s) have been published receiving 452754 citation(s). more

Topics: Fuzzy logic, Fuzzy number, Fuzzy set more

Journal ArticleDOI: 10.1016/0020-0255(75)90017-1
Lotfi A. Zadeh1Institutions (1)
Abstract: One of the fundamental tenets of modern science is that a phenomenon cannot be claimed to be well understood until it can be characterized in quantitative terms.l Viewed in this perspective, much of what constitutes the core of scientific knowledge may be regarded as a reservoir of concepts and techniques which can be drawn upon to construct mathematical models of various types of systems and thereby yield quantitative information concerning their behavior. more

12,259 Citations

Journal ArticleDOI: 10.1016/J.INS.2009.03.004
Abstract: In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. more

Topics: Metaheuristic (60%), Best-first search (59%), Search algorithm (59%) more

4,225 Citations

Journal ArticleDOI: 10.1016/S0020-0255(71)80005-1
Lotfi A. Zadeh1Institutions (1)
Abstract: The notion of ''similarity'' as defined in this paper is essentially a generalization of the notion of equivalence. In the same vein, a fuzzy ordering is a generalization of the concept of ordering. For example, the relation x @? y (x is much larger than y) is a fuzzy linear ordering in the set of real numbers. More concretely, a similarity relation, S, is a fuzzy relation which is reflexive, symmetric, and transitive. Thus, let x, y be elements of a set X and @m"s(x,y) denote the grade of membership of the ordered pair (x,y) in S. Then S is a similarity relation in X if and only if, for all x, y, z in X, @m"s(x,x) = 1 (reflexivity), @m"s(x,y) = @m"s(y,x) (symmetry), and @m"s(x,z) >= @? (@m"s(x,y) A @m"s(y,z)) (transitivity), where @? and A denote max and min, respectively. ^y A fuzzy ordering is a fuzzy relation which is transitive. In particular, a fuzzy partial ordering, P, is a fuzzy ordering which is reflexive and antisymmetric, that is, (@m"P(x,y) > 0 and x y) @? @m"P(y,x) = 0. A fuzzy linear ordering is a fuzzy partial ordering in which x y @? @m"s(x,y) > 0 or @m"s(y,x) > 0. A fuzzy preordering is a fuzzy ordering which is reflexive. A fuzzy weak ordering is a fuzzy preordering in which x y @? @m"s(x,y) > 0 or @m"s(y,x) > 0. Various properties of similarity relations and fuzzy orderings are investigated and, as an illustration, an extended version of Szpilrajn's theorem is proved. more

2,369 Citations

Journal ArticleDOI: 10.1016/J.INS.2014.01.015
C. L. Philip Chen1, Chun-Yang Zhang1Institutions (1)
Abstract: It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore's Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as data-intensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. © 2014 Elsevier Inc. All rights reserved. more

Topics: Big data (61%), e-Science (59%), Data visualization (54%) more

2,160 Citations

Journal ArticleDOI: 10.1016/J.INS.2006.06.003
Zdziasław Pawlak1, Andrzej Skowron1Institutions (1)
Abstract: Worldwide, there has been a rapid growth in interest in rough set theory and its applications in recent years. Evidence of this can be found in the increasing number of high-quality articles on rough sets and related topics that have been published in a variety of international journals, symposia, workshops, and international conferences in recent years. In addition, many international workshops and conferences have included special sessions on the theory and applications of rough sets in their programs. Rough set theory has led to many interesting applications and extensions. It seems that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, knowledge discovery, decision analysis, and expert systems. In the article, we present the basic concepts of rough set theory and point out some rough set-based research directions and applications. more

Topics: Dominance-based rough set approach (68%), Rough set (60%), Mereology (52%) more

1,899 Citations

No. of papers from the Journal in previous years

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Journal's top 5 most impactful authors

Shyi-Ming Chen

73 papers, 2.8K citations

Zeshui Xu

53 papers, 6.9K citations

Francisco Herrera

47 papers, 5.7K citations

Ronald R. Yager

36 papers, 1.8K citations

Hamido Fujita

35 papers, 2.2K citations

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