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Assessing modular structure of legacy code based on mathematical concept analysis
, 1997
"... We apply mathematical concept analysis in order to modularize legacy code. By analysing the relation between procedures and global variables, a socalled concept lattice is constructed. The paper explains how module structures show up in the lattice, and how the lattice can be used to assess cohesio ..."
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Cited by 113 (3 self)
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We apply mathematical concept analysis in order to modularize legacy code. By analysing the relation between procedures and global variables, a socalled concept lattice is constructed. The paper explains how module structures show up in the lattice, and how the lattice can be used to assess cohesion and coupling between module candidates. Certain algebraic decompositions of the lattice can lead to automatic generation of modularization proposals. The method is applied to several examples written in Modula2, Fortran, and Cobol; among them a>100kloc aerodynamics program.
Rough Sets: A Tutorial
, 1998
"... A rapid growth of interest in rough set theory [290] and its applications can be lately seen in the number of international workshops, conferences and seminars that are either directly dedicated to rough sets, include the subject in their programs, or simply accept papers that use this approach t ..."
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Cited by 55 (3 self)
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A rapid growth of interest in rough set theory [290] and its applications can be lately seen in the number of international workshops, conferences and seminars that are either directly dedicated to rough sets, include the subject in their programs, or simply accept papers that use this approach to solve problems at hand. A large number of high quality papers on various aspects of rough sets and their applications have been published in recent years as a result of this attention. The theory has been followed by the development of several software systems that implement rough set operations. In Section 12 we present a list of software systems based on rough sets. Some of the toolkits, provide advanced graphical environments that support the process of developing and validating rough set classifiers. Rough sets are applied in many domains, such as, for instance, medicine, finance, telecommunication, vibration analysis, conflict resolution, intelligent agents, image analysis, p...
Conceptual Graphs and Formal Concept Analysis
, 1997
"... . It is shown how Conceptual Graphs and Formal Concept Analysis may be combined to obtain a formalization of Elementary Logic which is useful for knowledge representation and processing. For this, a translation of conceptual graphs to formal contexts and concept lattices is described through an exam ..."
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Cited by 53 (7 self)
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. It is shown how Conceptual Graphs and Formal Concept Analysis may be combined to obtain a formalization of Elementary Logic which is useful for knowledge representation and processing. For this, a translation of conceptual graphs to formal contexts and concept lattices is described through an example. Using a suitable mathematization of conceptual graphs, basics of a unified mathematical theory for Elementary Logic are proposed. Contents 1. Formalization of Elementary Logic 2. From Conceptual Graphs to Formal Contexts 3. Mathematization of Conceptual Structures 1 Formalization of Elementary Logic Conceptual Graphs and Formal Concept Analysis have been used both for knowledge representation and processing in a large extent. This has caused the desire to combine the two approaches for deriving benefits from both disciplines and their experiences. There is even a fundamental reason for associating Conceptual Graphs and Formal Concept Analysis which lies in their farback reaching root...
Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods
, 1998
"... this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing (cf. [29],[30]). Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data a ..."
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Cited by 37 (15 self)
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this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing (cf. [29],[30]). Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last 18 years. This approach relies on the pragmatic philosophy of Ch. S. Peirce [15] who claims that we can only analyze and argue within restricted contexts where we always rely on preknowledge and common sense. The development of Formal Concept Analysis led to the software system TOSCANA, which is presented as a CKDD tool in this paper. TOSCANA is a flexible navigation tool that allows dynamic browsing through and zooming into the data. It supports the exploration of large databases by visualizing conceptual aspects inherent to the data. We want to clarify that CKDD can be understood as a humancentered approach of Knowledge Discovery in Databases. The actual discussion about humancentered Knowledge Discovery is therefore briefly summarized in Section 1
Attribute Exploration With Background Knowledge
 Theoretical Computer Science
, 1996
"... this article is to describe a generalized version of a well known knowledge acquistion method, called attribute exploration. To get a rough idea of what these explorations are about, imagine you want to classify some collection G of items according to selected properties. For example, G could be a c ..."
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Cited by 33 (1 self)
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this article is to describe a generalized version of a well known knowledge acquistion method, called attribute exploration. To get a rough idea of what these explorations are about, imagine you want to classify some collection G of items according to selected properties. For example, G could be a class of mathematical structures, e.g. groups, to be classified by structural properties like "commutative", "nilpotent", etc. Or G could consist of technical devices, car engines for example, and the attributes may reflect properties such as reliability, weight, price, and so on. But G might also be a set of persons, perhaps the students of your university, and the classifying attributes may be field of study, age, degree, etcetera. Attribute exploration then would help you to explore the implicational logic of these attributes.
Applied Lattice Theory: Formal Concept Analysis
 In General Lattice Theory, G. Grätzer editor, Birkhäuser
, 1997
"... then the theory. Thereby, Formal Concept Analysis has created results that may be of interest even without considering the applications by which they were motivated. For proofs, citations, and further details we refer to [2]. 1 Formal contexts and concept lattices A triple (G; M; I) is called a for ..."
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Cited by 32 (0 self)
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then the theory. Thereby, Formal Concept Analysis has created results that may be of interest even without considering the applications by which they were motivated. For proofs, citations, and further details we refer to [2]. 1 Formal contexts and concept lattices A triple (G; M; I) is called a formal context if G and M are sets and I ` G\ThetaM is a binary relation between G and M . We call the elements of G objects, those of M attributes, and I the incidence of the context (G; M; I). For A ` G, 1 we define A 0 := fm 2 M j (g; m) 2 I for all g 2 Ag<F12.38
SpecificationBased Browsing of Software Component Libraries
 in Proceedings of ASE
, 1999
"... . Specificationbased retrieval provides exact contentoriented access to component libraries but requires too much deductive power. Specificationbased browsing evades this bottleneck by moving any deduction into an offline indexing phase. In this paper, we show how match relations are used to bui ..."
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Cited by 31 (2 self)
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. Specificationbased retrieval provides exact contentoriented access to component libraries but requires too much deductive power. Specificationbased browsing evades this bottleneck by moving any deduction into an offline indexing phase. In this paper, we show how match relations are used to build an appropriate index and how formal concept analysis is used to build a suitable navigation structure. This structure has the singlefocus property (i.e., any sensible subset of a library is represented by a single node) and supports attributebased (via explicit component properties) and objectbased (via implicit component similarities) navigation styles. It thus combines the exact semantics of formal methods with the interactive navigation possibilities of informal methods. Experiments show that current theorem provers can solve enough of the emerging proof problems to make browsing feasible. The navigation structure also indicates situations where additional abstractions are required ...
Fast concept analysis
 Working with Conceptual Structures – Contributions to ICCS 2000
, 2000
"... Formal concept analysis is increasingly used for large contexts that are built by programs. This paper presents an efficient algorithm for concept analysis that computes concepts together with their explicit lattice structure. An experimental evaluation uses randomly generated contexts to compare th ..."
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Cited by 30 (1 self)
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Formal concept analysis is increasingly used for large contexts that are built by programs. This paper presents an efficient algorithm for concept analysis that computes concepts together with their explicit lattice structure. An experimental evaluation uses randomly generated contexts to compare the running time of the presented algorithm with two other algorithms. Running time increases quadratically with the number of concepts, but with a small quadratic component. At least contexts with sparsely filled context tables cause concept lattices grow quadratically with respect to the size of their base relation. The growth rate is controlled by the density of context tables. Modest growth combined with efficient algorithms lead to fast concept analysis. 1
Logical Scaling in Formal Concept Analysis
 Conceptual structures: ful Peirce's dream. LNAI 1257
, 1997
"... . Logical scaling is a new method to transform data matrices which are based on objectattributevaluerelationships into data matrices from which conceptual hierarchies can be explored. The derivation of concept lattices is determined by terminologies expressed in a formallogical language. Publishe ..."
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Cited by 22 (2 self)
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. Logical scaling is a new method to transform data matrices which are based on objectattributevaluerelationships into data matrices from which conceptual hierarchies can be explored. The derivation of concept lattices is determined by terminologies expressed in a formallogical language. Published in: D. Lukose et.al. (eds.): Conceptual Structures: Fulfilling Peirce's Dream. Proceedings of the ICCS'97, LNAI 1257, Springer, Berlin 1997, 332341. 1 Introduction The aim of formal concept analysis is to explore conceptual patterns in empirical data contexts. Methods have been developed to find conceptual hierachies and to represent them in line diagrams based on concept lattices (cf. [Wi82], [GW96]). These methods can be of great interest for knowledge representation and data mining. Concept lattices can also be relevant as principled ways to structure the type lattices used for conceptual graphs. In general, there is no immediate, "automatic" way to derive the conceptual structures o...
Conceptual Structures of Multicontexts
, 1996
"... Formal Concept Analysis is based on a formalization of context. Since there are situations where the consideration of one context is not sufficient, it is desirable to introduce a formalization of a network of contexts. In this paper, such formalization is given by the notion of multicontext. The ai ..."
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Cited by 17 (1 self)
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Formal Concept Analysis is based on a formalization of context. Since there are situations where the consideration of one context is not sufficient, it is desirable to introduce a formalization of a network of contexts. In this paper, such formalization is given by the notion of multicontext. The aim of the paper is to offer a first study of multicontexts and their conceptual structures; in particular, descriptions of conceptual coherences within the formalized network of contexts are presented. The theoretical considerations are illustrated by examples.