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Information retrieval using a novel concept similarity in formal concept analysis
(2014-11-05)
Muangprathub, Jirapond
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Boonjing, Veera
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Pattaraintakorn, Puntip
Concept lattices constructed by formal concept analysis have been successfully applied in structuring stored information to facilitate its retrieval. However, the models used in retrieval determine the results, and we propose an improved retrieval model to rank query results. The proposed concept similarity enables concept approximation, using both attribute extent and object intent, as well as occurrence frequencies in formal concepts. This approach avoids the computation of a Hasse diagram, only using the concepts of a formal context in query-based retrieval.
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A new case-based classification using incremental concept lattice knowledge
(2013-01-01)
Muangprathub, Jirapond
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Boonjing, Veera
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Pattaraintakorn, Puntip
This paper proposes a new case-based classification system with an incremental knowledge base. The new system employs a concept lattice with formal concept analysis as a knowledge structure. The paper also proposes a new efficient algorithm for knowledge construction as well as an effective retrieval method for formal concepts. The proposed retrieval method uses a concept similarity measure based on an appearance frequency of formal concepts. In addition, we provide a mathematical proof that the similarity measure satisfies a formal similarity metric definition. Experiment results on standard datasets show that our classifier with the proposed similarity measure gives accuracy better than with other existing similarity measures. © 2012 Elsevier B.V.
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A new similarity measure in formal concept analysis for case-based reasoning
(2012-01-01)
Tadrat, Jirapond
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Boonjing, Veera
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Pattaraintakorn, Puntip
In this work, we aim at developing a better knowledge base by using formal concept analysis (FCA) and propose its new similarity measure based on vector model for case-based reasoning (CBR). The features of our proposed approaches are illustrated using a part of CBR system for both classification and problem-solving. Concept lattice knowledge base provides more accuracy classification for hierarchical data structure when comparing with non-hierarchical data structure. Dependency induced from our concept lattice knowledge base can help to suggest informative solutions for problem-solving CBR. In addition, our similarity measure improves the accuracy of classification CBR significantly when we perform experiments on the UCI data sets with cross validation. © 2011 Elsevier Ltd. All rights reserved.
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An extension of rough set approximation to flow graph based data analysis
(2010-12-01)
Chitcharoen, Doungrat
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Pattaraintakorn, Puntip
This paper concerns some aspects of mathematical flow graph based data analysis. In particular, taking a flow graph view on rough sets' categories and measures leads to a new methodology of inductively reasoning form data. This perspective shows interesting relationships and properties among rough set, flow graphs and inverse flow graphs. A possible car dealer application is outlined and discussed. Evidently, our new categories and measures assist and alleviate some limitations in flow graphs to discover new patterns and explanations. © 2010 Springer-Verlag Berlin Heidelberg.
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Novel matrix forms of rough set flow graphs with applications to data integration
(2010-11-01)
Chitcharoen, Doungrat
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Pattaraintakorn, Puntip
Pawlak's flow graphs have attracted both practical and theoretical researchers because of their ability to visualize information flow. In this paper, we invent a new schema to represent throughflow of a flow graph and three coefficients of both normalized and combined normalized flow graphs in matrix form. Alternatively, starting from a flow graph with its throughflow matrix, we reform Pawlak's formulas to calculate these three coefficients in flow graphs by using matrix properties. While traditional algorithms for computing these three coefficients of the connection are exponential in l, an algorithm using our matrix representation is polynomial in l, where l is the number of layers of a flow graph. The matrix form can simplify computation, improve time complexity, alleviate problems due to missing coefficients and hence help to widen the applications of flow graphs. Practically, data sets often reside at different sources (heterogeneous data sources). Their individual analysis at each source is inadequate and requires special treatment. Hence, we introduce a composition method for flow graphs and corresponding formulas for calculating their coefficients which can omit some data sharing. We provide a real-world experiment on the Promotion of Academic Olympiads and Development of Science Education Foundation (POSN) data set which illustrates a desirable outcome and the advantages of the proposed matrix forms and the composition method. © 2010 Elsevier Ltd. All rights reserved.
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Entropy measures of flow graphs with applications to decision trees
(2009-08-27)
Pattaraintakorn, Puntip
Entropy is a fundamental principle in many disciplines such as information theory, thermodynamics, and more recently, artificial intelligence. In this article, a measure of entropy on Pawlak's mathematical flow graph is introduced. The predictability and quality of a flow graph can be derived directly from the entropy. An application to decision tree generation from a flow graph is examined. In particular, entropy measures on flow graphs lead to a new methodology of reasoning from data and shows rigorous relationships between flow graphs, entropy and decision trees. © 2009 Springer Berlin Heidelberg.
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A new case-based classifier system using rough formal concept analysis
(2008-12-29)
Pattaraintakorn, Puntip
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Boonjing, Veera
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Tadrat, Jirapond
Rough set theory and formal concept analysis were invented by Pawlak and Wide in the 1980s and have been applied successfully in several domains. In this paper, we propose a new case-based classifier system based on an integrated rough set theory and formal concept analysis technique. We focus on the construction of a better knowledge base to produce the classification rules. Our system employs rough set theory to discover reduced cases. We then formulate a knowledge base with hierarchical structure by using formal concept analysis. The result is a concept lattice knowledge base embedded in our case-based classifier. We can generate classification rules from implications and subconcept-superconcept relations inside the obtained concept lattice. An illustrative example and a case study are provided to demonstrate the feasibility and applicability of our system. The advantages of our system are thus a better knowledge base for new problem classification and the flexibility to learn new rules. © 2008 IEEE.
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Knowledge discovery by rough sets mathematical flow graphs and its extension
(2008-12-01)
Chitchareon, Doungrat
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Pattaraintakorn, Puntip
Mathematical rough set theory has attracted both practical and theoretical researchers. A significant extension of rough set theory is called flow graphs. It is a knowledge representation in the form of information flow. Flow graph is a promising approach to analyze data flow, decision trees, decision rules, probability learning, etc. In this article, we present their connections to pertinent techniques and propose a new extension to association rules. Two new propositions are used to reveal the relationship between flow graphs and association rules. We conduct experiment on real-world data collected from POSN with the evaluation. We discuss some important properties of flow graphs, with examples throughout.
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Building classification rules for case-based classifier using fuzzy sets and formal concept analysis
(2008-12-01)
Tadrat, Jirapond
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Boonjing, Veera
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Pattaraintakorn, Puntip
The focus of this paper is a construction of better knowledge base in case-based classifier system. Our knowledge base structure is based on concept lattice where rules are built from its subconcept-superconcept relation. Since the lattice can only be constructed from inputs with binary attributes, descriptive and numeric attributes must be transformed to binary attributes. In this paper, we propose the transformation of numeric attributes to descriptive attributes using fuzzy set theory. We experiment on benchmark data sets, Car and Iris, to determine the performance in term of number of rules used and classification precision. The results show that trend of accuracy is proportional to the size of learning inputs. The number of rules used is relatively small compared with size of training data. Our case-based classifier produces very promising results in practice and can classify the new problem more accurate than traditional classifiers. Copyright 2008 ACM.
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Analysis of distributed databases with a hybrid rough sets approach
(2008-11-24)
Pattaraintakorn, Puntip
The aim of this paper is to offer mathematical proofs of Pawlak's rough set theory about distributed knowledge based on rough sets and relational databases. A case study on actual self-reported geriatric data for survival analysis is presented to provide a computational evidence of the distributed knowledge. Risk factors, prolongation time prediction rules and validation are also computed and discussed. We illustrate that dividing a decision table (or database) into smaller units will in general result in the loss of some information by rough set theory. ©2008 IEEE.

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