Sadok Ben Yahia | University of Tunis El Manar (original) (raw)
Papers by Sadok Ben Yahia
Les outils de l’analyse en ligne (OLAP) permettent à l’utilisateur de réaliser des tâches explora... more Les outils de l’analyse en ligne (OLAP) permettent à l’utilisateur de
réaliser des tâches exploratoires dans les cubes de données. Cependant, ils n’offrent
aucun moyen pour la prédiction ou l’explication des faits. En vue de renforcer
le processus de l’aide à la décision, plusieurs travaux ont proposé l’extension
de l’analyse en ligne à des capacités plus avancées. Dans cet article, nous pro-
posons une nouvelle approche d’extension de l’analyse en ligne à des capacités
de prédiction à deux phases. La première est une phase de réduction des dimen-
sions des cubes de données, qui repose sur l’analyse en composantes principales
(ACP). La deuxième est une phase de prédiction dans laquelle nous introdui-
sons une nouvelle architecture de percéptrons multicouches (PMC). Notre étude
expérimentale a montré une capacité de prédiction prometteuse, ainsi qu’une
bonne robustesse dans le cas d’un cube épars.
On-line Analytical Processing (OLAP) represents a good applications package to explore and naviga... more On-line Analytical Processing (OLAP) represents a good applications package to explore and navigate into data cubes. Though, it is limited to exploratory tasks. It does not assist the decision maker in performing information investigation. Thus, various studies have been trying to extend OLAP to new capabilities by coupling it with data mining algorithms. Our current proposal stands within this trend. It has two major contributions. First, a Multi-perspectives Cube Exploration Framework (MCEF) is introduced. It is a generalized framework designed to assist the application of classical data mining algorithm on OLAP cubes. Second, a Neural Approach for Prediction over High-dimensional Cubes (NAP-HC) is also introduced, which extends Modular Neural Networks (MNN)s architecture to multidimensional context of OLAP cubes, to predict non-existent measures. A preprocessing stage is embedded in NAP-HC to assist it in facing up the challenges arising from the particu-larity of OLAP cubes. It consists of an OLAP oriented cube exploration strategy coupled with a dimensions reduction step that reposes on the Principal Component Analysis (PCA). Carried out experiments highlight the efficiency of MCEF in assisting the application of MNNs on OLAP cubes and the high predictive capabilities of NAP-HC.
OLAP techniques provide efficient solutions to navigate through data cubes. However, they are not... more OLAP techniques provide efficient solutions to navigate through
data cubes. However, they are not equipped with frameworks that empower
user investigation of interesting information. They are restricted
to exploration tasks.
Recently, various studies have been trying to extend OLAP to new capabilities
by coupling it with data mining algorithms. However, most of
these algorithms are not designed to deal with sparsity, which is an unavoidable
consequence of the multidimensional structure of OLAP cubes.
In [1], we proposed a novel approach that embeds Multilayer Perceptrons
into OLAP environment to extend it to prediction. This approach has
largely met its goals with limited sparsity cubes. However, its performances
have decreased progressively with the increase of cube sparsity.
In this paper, we propose a substantially modified version of our previous
approach called NAP-SC (Neural Approach for Prediction over Sparse
Cubes). Its main contribution consists in minimizing sparsity effect on
measures prediction process through the application of a cube transformation
step, based on a dedicated aggregation technique.
Carried out experiments demonstrate the effectiveness and the robustness
of NAP-SC against high sparsity data cubes.
In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good application... more In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good applications package that empowers decision makers to explore and navigate into a multidimensional structure of precomputed measures, which is referred to as a Data Cube. Though, OLAP is poorly equipped for forecasting and predicting empty measures of data cubes. Usually, empty measures translate inexistent facts in the DW and in most cases are a source of frustration for enterprise managements, especially when strategic decisions need to be taken. In the recent years, various studies have tried to add prediction capabilities to OLAP applications. For this purpose, generally, Data Mining and Machine Learning methods have been widely used to predict new measures' values in DWs. In this paper, we introduce a novel approach attempting to extend OLAP to a prediction application. Our approach operates in two main stages. The first one is a preprocessing one that makes use of the Principal Component Analysis (PCA) to reduce the dimensionality of the data cube and then generates ad hoc training sets. The second stage proposes a novel OLAP oriented architecture of Multilayer Perceptron Networks (MLP) that learns from each training set and comes out with predicted measures of inexistent facts. Carried out experiments demonstrate the effectiveness of our proposal and the performance of its predictive capabilities.
Lecture Notes in Computer Science, 2005
The steady growth in the size of data has encouraged the emergence of advanced main memory trie-b... more The steady growth in the size of data has encouraged the emergence of advanced main memory trie-based data structures. Concurrently, more acute knowledge extraction techniques are devised for the discovery of compact and lossless knowledge formally expressed by generic bases. In this paper, we present an approach for deriving generic bases of association rules. Using this approach, we construct small partially ordered sub-structures. Then, these ordered sub-structures are parsed to derive, in a straightforward manner, local generic association bases. Finally, local bases are merged to generate the global one. Extensive experiments carried out essentially showed that the proposed data structure allows to generate a more compact representation of an extraction context comparatively to existing approaches in literature.
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2000
... The function Gen-next takes as argument the set of fuzzy concepts FC; and computes the set CF... more ... The function Gen-next takes as argument the set of fuzzy concepts FC; and computes the set CFCi+l containing all (i + 1)-fuzzy itemsets, which will be used as fuzzy generators, during the next its-eration. Function Gen-next Input : CFi Begin CFCi+l = Apriori-Gen(CFi) 1 ...
The fingerprint matching is a very important step in a fingerprint recognition system. In this pa... more The fingerprint matching is a very important step in a fingerprint recognition system. In this paper, we present a new fingerprint matching algorithm based on principles minutiae local structures. This approach is an automated method of a human expert to examine relations between positions of local Minutiae (Minutiae local structure), and finally confirmed this verification using the global structure, composed by all the minutiae. The proposed algorithm is robust to the non-linear distortion and is based on the minutiae local structure that is invariant under rotation and translation motion. Indeed, this structure includes the position as well as the directional characteristics of minutiae relatively to neighboring ones. The obtained experimental results showed that the proposed method makes better the matching step.
The proposition in the title of this paper is intended to draw a link between psychological proce... more The proposition in the title of this paper is intended to draw a link between psychological processes involved in aesthetic gestural performance (e.g. music, dance) for both performers and perceivers. In the performance scenario, the player/dancer/etc., perceptually guides their actions, and acquires the skill for a performance through their previous perceptions. On the other side, the perceiver watching, listening to and experiencing another's motor performance, simulates the actions of the performance within the range of their own motor capabilities. These phenomena are possible due to common mechanisms of action and perception, and in tandem provide the basis for the rich experience of gestural performance.
Le principal inconvénient des approches classiques de génération des règles associatives réside d... more Le principal inconvénient des approches classiques de génération des règles associatives réside dans le fait qu'ils génèrent un nombre exorbitant de règles, rendant leur visualisation une tâche difficile. En outre, il existe très peu de travaux sur l'aspect exploitation et visualisation de ces règles comparativement au nombre de travaux dédiés à l'extraction de ces règles. Dans ce papier, on s'intéresse en particulier à la présentation d'un prototype de visualisation de bases génériques de règles associatives, appelé GERVIS. La principale originalité de GERVIS réside dans le fait qu'il utilise une méta-connaissance, formellement exprimée par un ensemble de règles floues. Cette méta-connaissance permet une exploration "scalable" et coopérative des bases génériques de règles. En plus de l'affichage à la demande explicite des règles associatives dérivables, est affichée une connaissance additionnelle matérialisant des connexions sémantiques entre règles. Ainsi, cette connaissance additionnelle permet d'améliorer l'interaction Homme / machine. ABSTRACT. The extremely large number of association rules that can be drawn from -even reasonably sizeddatasets bootstrapped the development of more acute techniques or methods to reduce the size of the reported rule sets. In order to be reliable in a decision making process, such discovered rules have to be both concise and easily understandable for users, and/or as an input to visualization tools. In this paper, we present, GERVIS, a graphical visualization prototype for handling generic bases of association rules. GERVIS's main originality is based on the fact that it uses a back-end meta-knowledge formally expressed by means of fuzzy meta-association rules. This meta-knowledge allows a cooperative exploration of generic bases of association rules. Besides displaying on user demand associated derivable rules, the additional knowledge highlighting connected rules to a user-selected rule provides an improvement of man-machine interaction.
L'alignement d'ontologies représente un grand intérêt dans le domaine de la gestion des connaissa... more L'alignement d'ontologies représente un grand intérêt dans le domaine de la gestion des connaissances hétérogènes. La littérature du domaine propose plusieurs méthodes d'alignement d'ontologies. Ces méthodes exploitent différents formats d'ontologies mais très peu s'intéressent au format OWL-DL. L'alignement d'ontologies repose sur le calcul des mesures de similarité. Ce papier décrit une nouvelle méthode d'alignement d'ontologies OWL-DL. Elle propose une approche d'alignement d'ontologies qui définit un modèle global de calcul de similarité. Nous présentons aussi une discussion sur les résultats d'expérimentations réalisées sur des bases test d'ontologie. ABSTRACT. Ontology Matching is of great interest in knowledge management domain especially when dealing with heterogenous knowledge. Different approaches have been reported for ontology alignment. Those approaches are based on similarity measurements. They also deal with different types of ontology format. This paper describes a novel ontology alignment method for OWL-DL format. The new method used a different approach that consists in computing local and global similarities. A thorough experimentation of this method has been conducted on different standard benchmarks, and the results are presented and discussed. MOTS-CLÉS : Alignement d'ontologies, similarités locale et globale, similarité structurelle, OWL-DL.
International Journal of Foundations of Computer Science
A thorough scrutiny of the literature dedicated to association rule mining highlights that a dete... more A thorough scrutiny of the literature dedicated to association rule mining highlights that a determined effort focused so far on mining the co-occurrence relations between items, i.e., conjunctive patterns. In this respect, disjunctive patterns presenting knowledge about complementary occurring items were neglected in the literature. Nevertheless, recently a growing number of works is shedding light on their importance for the sake of providing a richer knowledge for users. For this purpose, we propose in this paper a new tool, called GARM, aiming at building a partially ordered structure amongst some particular disjunctive patterns, namely the disjunctive closed ones. Starting from this structure, deriving generalized association rules, i.e., those offering conjunctive, disjunctive and negative connectors between items, becomes straightforward. Our experimental study put the focus on the mining performances as well as the quantitative aspect and proved the utility of the proposed approach.
Dans le domaine de fouille de données, les règles d'association ont sollicité l'intérêt de plusie... more Dans le domaine de fouille de données, les règles d'association ont sollicité l'intérêt de plusieurs chercheurs vu leur utilité dans plusieurs secteurs tels que le management des entreprises, le marketing, la biologie, la robotique, etc. Afin de les extraire des données en entrée, organisées sous forme d'une base de transactions (ou contexte d'extraction), deux étapes sont nécessaires à savoir, l'extraction des ensembles d'items (ou itemsets) utiles pour l'utilisateur et la génération des règles d'association à partir de ces ensembles.
Traditional framework for mining association rules has pointed out the derivation of many redunda... more Traditional framework for mining association rules has pointed out the derivation of many redundant rules. In order to be reliable in a decision making process, such discovered rules have to be both concise and easily understandable for users, and/or an input to visualization tools. In this paper, we present a 3 graphical visualization prototype for handling generic bases of association rules. We discuss also the most adequate graphical visualization technique depending on the intrinsic structure of the generic bases of association rules. An interesting feature of the prototype is that it provides a "contextual" exploration of such rule set. Such additional displayed knowledge, based on the discovery of fuzzy metarules, enhances man-machine interaction by emulating a cooperative behavior.
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004
Ahstruct-Traditional framework for mining association rules has pointed out the derivation of man... more Ahstruct-Traditional framework for mining association rules has pointed out the derivation of many redundant rules. In order to be reliable in a decision making process, such discovered rules have to be concise and easily understandable for users or as well as an input to visualization tools. In this paper, we present a 3D Histograms -based visualization prototype for handling generic bases of association rules. An interesting feature of the prototjpe is that it provides a '*contextual" exploration of such rule set. Such additional displayed knowledge, based on the construction of fuzzy meta-rules. enhances man-machine interaction by emulating a cooperative behavior.
The problem of relevance and the usefulness of extracted association rules is becoming of primary... more The problem of relevance and the usefulness of extracted association rules is becoming of primary importance, since an overwhelming number of association rules may be derived. This paper proposes an algorithm, called GenAll, to build a formal concept lattice, in which each formal concept is "decorated" by its minimal generators. The main characteristic of this algorithm is to use a refinement process of upper cover lists to determine, in a simultaneous manner, the set of formal concepts, their underlying partial order and the set of minimal generators associated to each formal concept. Experimental results have showed that the proposed algorithm is specially efficient for dense formal contexts compared to that of Nourine et al.. Response times pointed out by GenAll algorithm largely outperform those of Nourine et al..
Les outils de l’analyse en ligne (OLAP) permettent à l’utilisateur de réaliser des tâches explora... more Les outils de l’analyse en ligne (OLAP) permettent à l’utilisateur de
réaliser des tâches exploratoires dans les cubes de données. Cependant, ils n’offrent
aucun moyen pour la prédiction ou l’explication des faits. En vue de renforcer
le processus de l’aide à la décision, plusieurs travaux ont proposé l’extension
de l’analyse en ligne à des capacités plus avancées. Dans cet article, nous pro-
posons une nouvelle approche d’extension de l’analyse en ligne à des capacités
de prédiction à deux phases. La première est une phase de réduction des dimen-
sions des cubes de données, qui repose sur l’analyse en composantes principales
(ACP). La deuxième est une phase de prédiction dans laquelle nous introdui-
sons une nouvelle architecture de percéptrons multicouches (PMC). Notre étude
expérimentale a montré une capacité de prédiction prometteuse, ainsi qu’une
bonne robustesse dans le cas d’un cube épars.
On-line Analytical Processing (OLAP) represents a good applications package to explore and naviga... more On-line Analytical Processing (OLAP) represents a good applications package to explore and navigate into data cubes. Though, it is limited to exploratory tasks. It does not assist the decision maker in performing information investigation. Thus, various studies have been trying to extend OLAP to new capabilities by coupling it with data mining algorithms. Our current proposal stands within this trend. It has two major contributions. First, a Multi-perspectives Cube Exploration Framework (MCEF) is introduced. It is a generalized framework designed to assist the application of classical data mining algorithm on OLAP cubes. Second, a Neural Approach for Prediction over High-dimensional Cubes (NAP-HC) is also introduced, which extends Modular Neural Networks (MNN)s architecture to multidimensional context of OLAP cubes, to predict non-existent measures. A preprocessing stage is embedded in NAP-HC to assist it in facing up the challenges arising from the particu-larity of OLAP cubes. It consists of an OLAP oriented cube exploration strategy coupled with a dimensions reduction step that reposes on the Principal Component Analysis (PCA). Carried out experiments highlight the efficiency of MCEF in assisting the application of MNNs on OLAP cubes and the high predictive capabilities of NAP-HC.
OLAP techniques provide efficient solutions to navigate through data cubes. However, they are not... more OLAP techniques provide efficient solutions to navigate through
data cubes. However, they are not equipped with frameworks that empower
user investigation of interesting information. They are restricted
to exploration tasks.
Recently, various studies have been trying to extend OLAP to new capabilities
by coupling it with data mining algorithms. However, most of
these algorithms are not designed to deal with sparsity, which is an unavoidable
consequence of the multidimensional structure of OLAP cubes.
In [1], we proposed a novel approach that embeds Multilayer Perceptrons
into OLAP environment to extend it to prediction. This approach has
largely met its goals with limited sparsity cubes. However, its performances
have decreased progressively with the increase of cube sparsity.
In this paper, we propose a substantially modified version of our previous
approach called NAP-SC (Neural Approach for Prediction over Sparse
Cubes). Its main contribution consists in minimizing sparsity effect on
measures prediction process through the application of a cube transformation
step, based on a dedicated aggregation technique.
Carried out experiments demonstrate the effectiveness and the robustness
of NAP-SC against high sparsity data cubes.
In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good application... more In the Data Warehouse (DW) technology, On-line Analytical Processing (OLAP) is a good applications package that empowers decision makers to explore and navigate into a multidimensional structure of precomputed measures, which is referred to as a Data Cube. Though, OLAP is poorly equipped for forecasting and predicting empty measures of data cubes. Usually, empty measures translate inexistent facts in the DW and in most cases are a source of frustration for enterprise managements, especially when strategic decisions need to be taken. In the recent years, various studies have tried to add prediction capabilities to OLAP applications. For this purpose, generally, Data Mining and Machine Learning methods have been widely used to predict new measures' values in DWs. In this paper, we introduce a novel approach attempting to extend OLAP to a prediction application. Our approach operates in two main stages. The first one is a preprocessing one that makes use of the Principal Component Analysis (PCA) to reduce the dimensionality of the data cube and then generates ad hoc training sets. The second stage proposes a novel OLAP oriented architecture of Multilayer Perceptron Networks (MLP) that learns from each training set and comes out with predicted measures of inexistent facts. Carried out experiments demonstrate the effectiveness of our proposal and the performance of its predictive capabilities.
Lecture Notes in Computer Science, 2005
The steady growth in the size of data has encouraged the emergence of advanced main memory trie-b... more The steady growth in the size of data has encouraged the emergence of advanced main memory trie-based data structures. Concurrently, more acute knowledge extraction techniques are devised for the discovery of compact and lossless knowledge formally expressed by generic bases. In this paper, we present an approach for deriving generic bases of association rules. Using this approach, we construct small partially ordered sub-structures. Then, these ordered sub-structures are parsed to derive, in a straightforward manner, local generic association bases. Finally, local bases are merged to generate the global one. Extensive experiments carried out essentially showed that the proposed data structure allows to generate a more compact representation of an extraction context comparatively to existing approaches in literature.
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2000
... The function Gen-next takes as argument the set of fuzzy concepts FC; and computes the set CF... more ... The function Gen-next takes as argument the set of fuzzy concepts FC; and computes the set CFCi+l containing all (i + 1)-fuzzy itemsets, which will be used as fuzzy generators, during the next its-eration. Function Gen-next Input : CFi Begin CFCi+l = Apriori-Gen(CFi) 1 ...
The fingerprint matching is a very important step in a fingerprint recognition system. In this pa... more The fingerprint matching is a very important step in a fingerprint recognition system. In this paper, we present a new fingerprint matching algorithm based on principles minutiae local structures. This approach is an automated method of a human expert to examine relations between positions of local Minutiae (Minutiae local structure), and finally confirmed this verification using the global structure, composed by all the minutiae. The proposed algorithm is robust to the non-linear distortion and is based on the minutiae local structure that is invariant under rotation and translation motion. Indeed, this structure includes the position as well as the directional characteristics of minutiae relatively to neighboring ones. The obtained experimental results showed that the proposed method makes better the matching step.
The proposition in the title of this paper is intended to draw a link between psychological proce... more The proposition in the title of this paper is intended to draw a link between psychological processes involved in aesthetic gestural performance (e.g. music, dance) for both performers and perceivers. In the performance scenario, the player/dancer/etc., perceptually guides their actions, and acquires the skill for a performance through their previous perceptions. On the other side, the perceiver watching, listening to and experiencing another's motor performance, simulates the actions of the performance within the range of their own motor capabilities. These phenomena are possible due to common mechanisms of action and perception, and in tandem provide the basis for the rich experience of gestural performance.
Le principal inconvénient des approches classiques de génération des règles associatives réside d... more Le principal inconvénient des approches classiques de génération des règles associatives réside dans le fait qu'ils génèrent un nombre exorbitant de règles, rendant leur visualisation une tâche difficile. En outre, il existe très peu de travaux sur l'aspect exploitation et visualisation de ces règles comparativement au nombre de travaux dédiés à l'extraction de ces règles. Dans ce papier, on s'intéresse en particulier à la présentation d'un prototype de visualisation de bases génériques de règles associatives, appelé GERVIS. La principale originalité de GERVIS réside dans le fait qu'il utilise une méta-connaissance, formellement exprimée par un ensemble de règles floues. Cette méta-connaissance permet une exploration "scalable" et coopérative des bases génériques de règles. En plus de l'affichage à la demande explicite des règles associatives dérivables, est affichée une connaissance additionnelle matérialisant des connexions sémantiques entre règles. Ainsi, cette connaissance additionnelle permet d'améliorer l'interaction Homme / machine. ABSTRACT. The extremely large number of association rules that can be drawn from -even reasonably sizeddatasets bootstrapped the development of more acute techniques or methods to reduce the size of the reported rule sets. In order to be reliable in a decision making process, such discovered rules have to be both concise and easily understandable for users, and/or as an input to visualization tools. In this paper, we present, GERVIS, a graphical visualization prototype for handling generic bases of association rules. GERVIS's main originality is based on the fact that it uses a back-end meta-knowledge formally expressed by means of fuzzy meta-association rules. This meta-knowledge allows a cooperative exploration of generic bases of association rules. Besides displaying on user demand associated derivable rules, the additional knowledge highlighting connected rules to a user-selected rule provides an improvement of man-machine interaction.
L'alignement d'ontologies représente un grand intérêt dans le domaine de la gestion des connaissa... more L'alignement d'ontologies représente un grand intérêt dans le domaine de la gestion des connaissances hétérogènes. La littérature du domaine propose plusieurs méthodes d'alignement d'ontologies. Ces méthodes exploitent différents formats d'ontologies mais très peu s'intéressent au format OWL-DL. L'alignement d'ontologies repose sur le calcul des mesures de similarité. Ce papier décrit une nouvelle méthode d'alignement d'ontologies OWL-DL. Elle propose une approche d'alignement d'ontologies qui définit un modèle global de calcul de similarité. Nous présentons aussi une discussion sur les résultats d'expérimentations réalisées sur des bases test d'ontologie. ABSTRACT. Ontology Matching is of great interest in knowledge management domain especially when dealing with heterogenous knowledge. Different approaches have been reported for ontology alignment. Those approaches are based on similarity measurements. They also deal with different types of ontology format. This paper describes a novel ontology alignment method for OWL-DL format. The new method used a different approach that consists in computing local and global similarities. A thorough experimentation of this method has been conducted on different standard benchmarks, and the results are presented and discussed. MOTS-CLÉS : Alignement d'ontologies, similarités locale et globale, similarité structurelle, OWL-DL.
International Journal of Foundations of Computer Science
A thorough scrutiny of the literature dedicated to association rule mining highlights that a dete... more A thorough scrutiny of the literature dedicated to association rule mining highlights that a determined effort focused so far on mining the co-occurrence relations between items, i.e., conjunctive patterns. In this respect, disjunctive patterns presenting knowledge about complementary occurring items were neglected in the literature. Nevertheless, recently a growing number of works is shedding light on their importance for the sake of providing a richer knowledge for users. For this purpose, we propose in this paper a new tool, called GARM, aiming at building a partially ordered structure amongst some particular disjunctive patterns, namely the disjunctive closed ones. Starting from this structure, deriving generalized association rules, i.e., those offering conjunctive, disjunctive and negative connectors between items, becomes straightforward. Our experimental study put the focus on the mining performances as well as the quantitative aspect and proved the utility of the proposed approach.
Dans le domaine de fouille de données, les règles d'association ont sollicité l'intérêt de plusie... more Dans le domaine de fouille de données, les règles d'association ont sollicité l'intérêt de plusieurs chercheurs vu leur utilité dans plusieurs secteurs tels que le management des entreprises, le marketing, la biologie, la robotique, etc. Afin de les extraire des données en entrée, organisées sous forme d'une base de transactions (ou contexte d'extraction), deux étapes sont nécessaires à savoir, l'extraction des ensembles d'items (ou itemsets) utiles pour l'utilisateur et la génération des règles d'association à partir de ces ensembles.
Traditional framework for mining association rules has pointed out the derivation of many redunda... more Traditional framework for mining association rules has pointed out the derivation of many redundant rules. In order to be reliable in a decision making process, such discovered rules have to be both concise and easily understandable for users, and/or an input to visualization tools. In this paper, we present a 3 graphical visualization prototype for handling generic bases of association rules. We discuss also the most adequate graphical visualization technique depending on the intrinsic structure of the generic bases of association rules. An interesting feature of the prototype is that it provides a "contextual" exploration of such rule set. Such additional displayed knowledge, based on the discovery of fuzzy metarules, enhances man-machine interaction by emulating a cooperative behavior.
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004
Ahstruct-Traditional framework for mining association rules has pointed out the derivation of man... more Ahstruct-Traditional framework for mining association rules has pointed out the derivation of many redundant rules. In order to be reliable in a decision making process, such discovered rules have to be concise and easily understandable for users or as well as an input to visualization tools. In this paper, we present a 3D Histograms -based visualization prototype for handling generic bases of association rules. An interesting feature of the prototjpe is that it provides a '*contextual" exploration of such rule set. Such additional displayed knowledge, based on the construction of fuzzy meta-rules. enhances man-machine interaction by emulating a cooperative behavior.
The problem of relevance and the usefulness of extracted association rules is becoming of primary... more The problem of relevance and the usefulness of extracted association rules is becoming of primary importance, since an overwhelming number of association rules may be derived. This paper proposes an algorithm, called GenAll, to build a formal concept lattice, in which each formal concept is "decorated" by its minimal generators. The main characteristic of this algorithm is to use a refinement process of upper cover lists to determine, in a simultaneous manner, the set of formal concepts, their underlying partial order and the set of minimal generators associated to each formal concept. Experimental results have showed that the proposed algorithm is specially efficient for dense formal contexts compared to that of Nourine et al.. Response times pointed out by GenAll algorithm largely outperform those of Nourine et al..