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Start Over Search Limiters Full Text Topic clustering Publication Year Range More than 50 years ago
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1. A Heuristic Approach to Inductive Inference in Fact Retrieval Systems.

Author

Skinner, C. William

Subjects
HEURISTIC programming, INFORMATION storage & retrieval systems, OPERATIONS research, METHODOLOGY, PROBABILITY theory, ALGORITHMS
Abstract

Heuristic procedures arc presented which have been developed to perform inferences by generalizing from available information. The procedures make use of a similarity structure which is imposed on the data base using nonnumerical clustering algorithms. They are implemented in a model fact retrieval system which uses a formal query language and a property-list data structure. A program of experiments is described wherein the procedures are used with test data bases which are altered by deleting part of the data and by purposely introducing false data. It is found that the system can infer the correct response under a variety of conditions involving incomplete and inconsistent data. [ABSTRACT FROM AUTHOR]

Published
1974
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3. The Quadratic Quotient Method: A Hash Code Eliminating Secondary Clustering.

Author

Bell, James R.

Subjects
HASHING, MACHINE ciphers, INFORMATION storage & retrieval systems, ERROR-correcting codes, COMPUTER software, ELECTRONIC file management, CONGRUENCES & residues
Abstract

Secondary clustering as a cause of hash code inefficiency is discussed, and a new hashing method based on its elimination is presented. Comparisons with previous methods are made both analytically and empirically. [ABSTRACT FROM AUTHOR]

Published
1970
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7. Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts

Author

Emmanuel Navarro, Henri Prade, Bruno Gaume, Cognition, Langues, Langage, Ergonomie (CLLE-LTC), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Cognition, Langues, Langage, Ergonomie (CLLE-ERSS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J)-Université Bordeaux Montaigne-Centre National de la Recherche Scientifique (CNRS), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Ecole Pratique des Hautes Etudes - EPHE (FRANCE), Université Bordeaux Montaigne (FRANCE), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Theoretical computer science, General Computer Science, formal concept analysis (FCA), Conceptual clustering, 0102 computer and information sciences, 02 engineering and technology, possibility theory, 01 natural sciences, Clustering, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], lcsh:QA75.5-76.95, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Clique-width, 3-dimensional matching, 0202 electrical engineering, electronic engineering, information engineering, Small worlds, Formal concept analysis, Cluster analysis, Mathematics, Logique en informatique, Bipartite graph, Possibility theory, business.industry, Voltage graph, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Informatique et langage, Intelligence artificielle, Apprentissage, Computational Mathematics, small world, 010201 computation theory & mathematics, bipartite graph, 020201 artificial intelligence & image processing, Artificial intelligence, Formal concept analysis (FCA), lcsh:Electronic computers. Computer science, Small world, business, clustering
Abstract

The paper first offers a parallel between two approaches to conceptual clustering, namely formal concept analysis (augmented with the introduction of new operators) and bipartite graph analysis. It is shown that a formal concept (as defined in formal concept analysis) corresponds to the idea of a maximal bi-clique, while sub-contexts, which correspond to independent "conceptual worlds" that can be characterized by means of the new operators introduced, are disconnected sub-graphs in a bipartite graph. The parallel between formal concept analysis and bipartite graph analysis is further exploited by considering "approximation" methods on both sides. It leads to suggest new ideas for providing simplified views of datasets, taking also inspiration from the search for approximate itemsets in data mining (with relaxed requirements), and the detection of communities in hierarchical small worlds. © 2013 Copyright the authors.

Published
1970