A framework for phishing attack identification using rough set and formal concept analysis

Complex and noisy financial eco-system requires reliable models and proven techniques to predict the market movements and investor decisions. This study uses competent soft computing techniques: rough set theory (RST) and formal concept analysis (FCA) to study the investors' preferences, behavioural drivers and their actual behaviour in Gold-ETF (G-ETF) market. G-ETF, though a safe-haven and an alternate for reducing portfolio risks, inherits all complexities of financial markets. The employed RST helps in generating decision rules; and FCA to identify key factors affecting investment decision. This study is first of its kind, as integration of the foresaid techniques was not employed to study financial behaviour, earlier. The study has analysed 250 responses of G-ETF investors, in 12 listed G-ETFs, to conclude with a rich insight on the investment decisions discretised by different decision rules, strongly recommending the combined use of RST and FCA for data driven decisions.

The theory introduced, presented and developed in this paper, is concerned with Rough Concept Analysis. This theory is a synthesis of the theory of Rough Sets pioneered by Zdzislaw Pawlak [10] with the theory of Formal Concept Analysis pioneered by Rudolf Wille [11]. The central notion in this paper of a rough formal concept combines in a natural fashion the two notions of rough set and formal concept — to use a slogan: “rough set + formal concept = rough formal concept”. This paper is an extension of the paper [5] presented at the international workshop on Rough Sets and Knowledge Discovery (RSKD'93). A related paper [8] using distributed constraints provides a synthesis of the two important data modeling techniques: conceptual scaling of Formal Concept Analysis, and Entity-Relationship database modeling. A follow-up paper [9] will extend rough concept analysis from formal contexts to distributed constraints.

The role of this ontology is to provide a shared vocabulary and semantics between domain knowledge and vision. Ontology presents semantics for interpreting data instances. Formal concept lattices and rough set theory are two kinds of complementary mathematical tools for data analysis and data processing. The paper proposes the method of constructiong ontology nodes information system based on rough formal concept analysis in order to constructing the ontology model of knowledge management. Experimental studies are done with rough set and FCA in constructing the numbers of ontology nodes. The experimental results indicate that this method has great promise and is showing that ROUGH SET is superior to FCA in building the numbers of ontology nodes.

Formal contexts is a common framework for rough set theory and formal concept analysis, and some rough set models in formal contexts have been proposed. In this paper, based on the theory of abstract approximation spaces presented by Cattaneo [1], a Brouwer orthocomplementation on the set of objects of a formal context is presented, as a result, a pair of new lower and upper rough approximation operators is introduced. Comparison between the new approximation operators and the existing approximation operators is made, and two necessary and sufficient conditions about equivalence of the operators are obtained. Relationships and algebraic structures among the definable subsets of these approximation operators are investigated.

Basic relationships between Rough Set Theory (RST) and Formal Concept Analysis (FCA) are discussed. Differences between the “partition oriented” RST and the “order oriented” FCA concerning the possibility of knowledge representation are investigated. The fundamental connection between RST and FCA is that the knowledge bases of RST and the scaled many-valued contexts of FCA are shown to be nearly equivalent.

The paper examines Formal Concept Analysis (FCA) and Rough Set Theory (RST) against the background of the theory of finite approximations of continuous topological spaces. We define the operators of FCA and RST by means of the specialisation order on elements of a topological space X which induces a finite approximation of X. On this basis we prove that FCA and RST together provide a semantics for tense logic S4.t. Moreover, the paper demonstrates that a topological space X cannot be distinguished from its finite approximation by means of the basic temporal language. It means that from the perspective of topology S4.t is a better account of approximate reasoning then unimodal logics, which have been typically employed.

This presentation discusses identification of attributes that are considered essential for a development of sustainable forest management practices in the Siberian forests. This goal is accomplished through an analysis of net primary production of phytomass (NPP), which is used to classify the Siberian ecoregions into compact and cohesive NPP performance classes. Rough Sets (RS) analysis is used as a data mining methodology for the evaluation of the Siberian forest database. In order to interpret relationships between various forest characteristics, so called interesting rules are generated on a basis of a reduced problem description.KeywordsRough setsknowledge discoverysustainable developmentforest management

In the context of multiple intelligences theory, intelligent data analysis of learning styles was based on rough set theory

Three-way conflict analysis: A unification of models based on rough sets and formal concept analysis

Rough set theory is an important tool to deal with imprecise, uncertainty and fuzzy information, and has gained great success in machine learning, data mining and intelligent data analysis. When it was proposed, many researchers worked on it in different views such as constructive methods, algebraic methods, formal concept analysis, and etc. We analyze it in view of entropy theory, and present a new concept - knowledge entropy. We discuss properties of it and apply it to discuss basic concepts in rough set theory. From the analysis, we construct a mapping between knowledge entropy theory and rough set theory, and we also hope to find effective algorithms by the application of it.

Rough set theory and formal concept analysis are two complementary mathematical tools for data analysis. In this paper, based on object oriented concept lattice, we first propose one kind of consistency of conditional concepts and decision concepts. Subsequently, notions of consistent set and reduct set of attributes are introduced, and some judgment theorems for consistent set are presented. Finally, discernibility matrix is employed to determine consistent sets and calculate reduct sets in consistent formal decision contexts.

During the analysis of stability heat conduction in the composite pipes, firstly, when the heat equation contained fuzzy and random uncertain parameters, interval equations of the heat conduction are presented in the rough set. Secondly, the error expecting of heat conduction equation is presented. Finally, with upper (lower) approximation in rough set, a new method of the rough set analysis to deal with the stability heat conduction is presented.

The definition of basic rough sets [3] depends upon either a single equivalence relation defined on a universe or several equivalence relations defined over the universe, taken one each taken at a time. In the view of granular computing, classical rough set theory is based upon single granulation. Extending this notion, a rough set model based on multi-granulations (MGRS) was introduced in [5]. In this approach, approximations of sets were defined through multiple equivalence relations on the universe and their properties were investigated. Using hybridization of fuzzy set [13] with rough set the concept of rough fuzzy set was introduced by Dubois and Prade [1]. Recently, a Rough Fuzzy Set Model was introduced and studied by Wu and Kou [12], which is based on Multiple Granulation. Topological properties of rough sets introduced by Pawlak in terms of their types were recently studied by Tripathy and Mitra [10]. These were extended to the context of incomplete multi granulation by Tripathy and Raghavan [11]. In this paper we introduce incomplete multigranulation on rough fuzzy sets, study their basic properties and extend the topological properties in [11] to this context. Our findings are true for both complete and incomplete fuzzy rough set models based upon multi granulation.KeywordsRough SetsFuzzy rough setsequivalence relationstolerance relationstype of rough setsmulti granular fuzzy rough sets

Rough-set-driven approach for attribute reduction in fuzzy formal concept analysis

Rough sets have been applied to many areas where multi-attribute data is needed to be analyzed to acquire knowledge for decision making. Web-based Support Systems (WSS) are a new research area that aims to support human activities and extend human physical limitations of information processing with Web technologies. The applications of rough set analysis for WSS is looked at in this article. In particular, our focus will be on Web-Based Medical Support Systems (WMSS). A WMSS is a support system that integrates medicine practices (diagnosis and surveillance) with computer science and Web technologies. We will explore some of the challenges of using rough sets in a WMSS and detail some of the applications of rough sets in analyzing medical data.