Rafik Taouil - Academia.edu (original) (raw)

Papers by Rafik Taouil

Research paper thumbnail of Querying Concept Lattices in Object Databases

Issues and Applications of Database Technology, 1998

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Research paper thumbnail of Restructuring Iceberg Lattice For Multilevel Analysis

2018 6th International Conference on Multimedia Computing and Systems (ICMCS), 2018

We introduce the hierarchy called MLIL for multilevel iceberg concept lattices, and show their us... more We introduce the hierarchy called MLIL for multilevel iceberg concept lattices, and show their use in data mining in very large transactional database, with ordered attributes. MLIL is a restructuring of the iceberg lattice, which respects the hierarchical levels of the itemsets after having plunged unwanted background knowledge at the output, and which are explicit by the order relation on the attributes. MLIL also serve as a new condensed representation of frequent itemsets for multilevel analysis, as a starting point for computing bases of generalized association rules, and as a visualization method for generalized association rules. MLIL is based on the lattice structure of the antichains lattice of the order on the attributes, and on the iceberg lattices and both are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery.

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Research paper thumbnail of Fast Computation of Concept lattices Using Data Mining Techniques

Knowledge Representation Meets Databases, 2000

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Research paper thumbnail of Closed Set Based Discovery of Small Covers for Association Rules

In this paper, we address the problem of the understandability and usefulness of the set of disco... more In this paper, we address the problem of the understandability and usefulness of the set of discovered association rules. This problem is important since real-life databases lead most of the time to several thousands of rules with high confidence. We thus propose new algorithms based on the Galois closed sets to limit the extraction to small informative covers for exact and approximate rules, and small structural covers for approximate rules. Once frequent closed itemsets - which constitute a generating set for both frequent itemsets and association rules - have been discovered, no additional database pass is needed to derive these covers. Experiments conducted on real-life databases show that these algorithms are efficient and valuable in practice.

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Research paper thumbnail of Computing Proper Implications

This paper presents the proper implications: all implications holding on a set with a minimal lef... more This paper presents the proper implications: all implications holding on a set with a minimal left-hand side and a one-item right-hand side. Although not the smallest representation, they are easily readable and allow for some efficient selection and projection (embedding) operations. The proposed algorithm, Impec, is designed to efficiently find proper implications given a set and a closure operator on this set. Additionally, it can be easily extended with a weight function or to compute embedded implications.

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Research paper thumbnail of Closed Set Based Dis overy of Small Coversfor Asso iation RulesNi olas

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Research paper thumbnail of Efficient Mining of Association Rulesusing Closed Itemset

ABSTRACT Discovering association rules is one of the most important task in data mining. Many eff... more ABSTRACT Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.

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Research paper thumbnail of A first study of the central role of the analyst in the knowledge discovery process in biology

Based on an application of symbolic data mining methods on a test database, we underline the role... more Based on an application of symbolic data mining methods on a test database, we underline the role played by the analyst in the knowledge discovery process. Encouraged by positive results, we plan to apply these methods on a large database for investigating the relationships between gene polymorphisms and cardiovascular diseases intermediate phenotypes.

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Research paper thumbnail of Conceptual Clustering with Iceberg Concept Lattices

We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in ... more We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery.

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Research paper thumbnail of Pruning closed itemset lattices for associations rules

Discovering association rules is one of the most important task in data mining and many efficient... more Discovering association rules is one of the most important task in data mining and many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning of the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning of the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census data, and performs reasonably well for market basket style data.

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Research paper thumbnail of Levelwise Search of Frequent Patterns with Counting Inference

In this paper,we address the problem of the efficiency of the main phase of most data mining appl... more In this paper,we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database, and we propose a new method called pattern counting inference, that allows to perform as few support counts as possible. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the Pascal algorithm that is an optimization of the simple and efficient Apriori Algorithm. Experiments comparing Pascal to the Apriori, Close and Max-Miner algorithms, each one representative of a frequent patterns discovery strategy, show that Pascal improves the efficiency of the frequent pattern extraction from correlated data and that it does not induce additional execution times when data is weakly correl...

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Research paper thumbnail of An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data

Database is a repository of information. Retrieving automatic patterns from the database provide ... more Database is a repository of information. Retrieving automatic patterns from the database provide the requisite information and are in great demand in various domains of science and engineering. The effective pattern mining methods such as pattern discovery and association rule mining have been developed and find its applicability in a wide gamut ranging from science to medical to military and to engineering applications. Contemporary methods of retrieval such as pattern discovery and association rule mining algorithms are useful only for retrieving the data. The limitations of using these techniques are that they are unable to provide a complete association and relationship among the diverse patterns that is retrieved. This paper attempts a solution to the above limitation by designing a new algorithm (CFIM) which generates closed frequent patterns and its associated data concurrently. CFIM makes explicit the relationship between the patterns and its associated data.

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Research paper thumbnail of Mining Bases for Association Rules using

We address the problem of the usefulness of the set of discovered association rules. This problem... more We address the problem of the usefulness of the set of discovered association rules. This problem is important since real-life databases yield most of the time several thousands of rules with high confidence. We propose new algorithms based on Galois closed sets to reduce the extraction to bases for exact and approximate rules. Once frequent closed itemsets -- which constitute a generating set for both frequent itemsets and association rules -- have been discovered, no additional database pass is needed to derive these bases. Experiments conducted on real-life databases show that these algorithms are e#cient and valuable in practice.

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Research paper thumbnail of Closed sets based discovery of small covers for association rules

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Research paper thumbnail of Lakhal: Fast computation of concept lattices using data mining techniques

2 1 Technische Universit"at Darmstadt, Fachbereich Mathematik,

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Research paper thumbnail of Lakhal: Fast computation of concept lattices using data mining techniques

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Research paper thumbnail of Towards an object database approach for managing concept lattices

Proceedings of the 16th International Conference on Conceptual Modeling, 1997

ABSTRACT The concept lattice is a conceptual model firstly introduced by Wille in formal concept ... more ABSTRACT The concept lattice is a conceptual model firstly introduced by Wille in formal concept analysis, a theory of concept formation derived from lattice and order theory. Various concept lattice based applications have been reported in several domains such as conceptual clustering, conceptual knowledge representation and acquisition, and information retrieval. In this paper, we propose an object database approach for managing concept lattices in these applications. The goal of our work is two-fold. First, we extend the concept lattice model by basic operations supporting concept analysis. These operations allow to search and discover data directly from the concept lattice. Then, we present an approach for modeling and querying concept lattices within an object database framework.

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Research paper thumbnail of Querying Concept Lattices in Object Databases

Iadt, 1998

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Research paper thumbnail of Computing iceberg concept lattices with T

Dke, 2002

... rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathema... more ... rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We ... 40]. 2. Formal concept analysis. Since ...

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Research paper thumbnail of PASCAL : un algorithme d extraction des motifs fr�quents

Tsi, 2002

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Research paper thumbnail of Querying Concept Lattices in Object Databases

Issues and Applications of Database Technology, 1998

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Restructuring Iceberg Lattice For Multilevel Analysis

2018 6th International Conference on Multimedia Computing and Systems (ICMCS), 2018

We introduce the hierarchy called MLIL for multilevel iceberg concept lattices, and show their us... more We introduce the hierarchy called MLIL for multilevel iceberg concept lattices, and show their use in data mining in very large transactional database, with ordered attributes. MLIL is a restructuring of the iceberg lattice, which respects the hierarchical levels of the itemsets after having plunged unwanted background knowledge at the output, and which are explicit by the order relation on the attributes. MLIL also serve as a new condensed representation of frequent itemsets for multilevel analysis, as a starting point for computing bases of generalized association rules, and as a visualization method for generalized association rules. MLIL is based on the lattice structure of the antichains lattice of the order on the attributes, and on the iceberg lattices and both are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Fast Computation of Concept lattices Using Data Mining Techniques

Knowledge Representation Meets Databases, 2000

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Closed Set Based Discovery of Small Covers for Association Rules

In this paper, we address the problem of the understandability and usefulness of the set of disco... more In this paper, we address the problem of the understandability and usefulness of the set of discovered association rules. This problem is important since real-life databases lead most of the time to several thousands of rules with high confidence. We thus propose new algorithms based on the Galois closed sets to limit the extraction to small informative covers for exact and approximate rules, and small structural covers for approximate rules. Once frequent closed itemsets - which constitute a generating set for both frequent itemsets and association rules - have been discovered, no additional database pass is needed to derive these covers. Experiments conducted on real-life databases show that these algorithms are efficient and valuable in practice.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Computing Proper Implications

This paper presents the proper implications: all implications holding on a set with a minimal lef... more This paper presents the proper implications: all implications holding on a set with a minimal left-hand side and a one-item right-hand side. Although not the smallest representation, they are easily readable and allow for some efficient selection and projection (embedding) operations. The proposed algorithm, Impec, is designed to efficiently find proper implications given a set and a closure operator on this set. Additionally, it can be easily extended with a weight function or to compute embedded implications.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Closed Set Based Dis overy of Small Coversfor Asso iation RulesNi olas

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Efficient Mining of Association Rulesusing Closed Itemset

ABSTRACT Discovering association rules is one of the most important task in data mining. Many eff... more ABSTRACT Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A first study of the central role of the analyst in the knowledge discovery process in biology

Based on an application of symbolic data mining methods on a test database, we underline the role... more Based on an application of symbolic data mining methods on a test database, we underline the role played by the analyst in the knowledge discovery process. Encouraged by positive results, we plan to apply these methods on a large database for investigating the relationships between gene polymorphisms and cardiovascular diseases intermediate phenotypes.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Conceptual Clustering with Iceberg Concept Lattices

We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in ... more We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Pruning closed itemset lattices for associations rules

Discovering association rules is one of the most important task in data mining and many efficient... more Discovering association rules is one of the most important task in data mining and many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning of the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning of the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census data, and performs reasonably well for market basket style data.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Levelwise Search of Frequent Patterns with Counting Inference

In this paper,we address the problem of the efficiency of the main phase of most data mining appl... more In this paper,we address the problem of the efficiency of the main phase of most data mining applications: The frequent pattern extraction. This problem is mainly related to the number of operations required for counting pattern supports in the database, and we propose a new method called pattern counting inference, that allows to perform as few support counts as possible. Using this method, the support of a pattern is determined without accessing the database whenever possible, using the supports of some of its sub-patterns called key patterns. This method was implemented in the Pascal algorithm that is an optimization of the simple and efficient Apriori Algorithm. Experiments comparing Pascal to the Apriori, Close and Max-Miner algorithms, each one representative of a frequent patterns discovery strategy, show that Pascal improves the efficiency of the frequent pattern extraction from correlated data and that it does not induce additional execution times when data is weakly correl...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An Efficient Mining Algorithm for Closed Frequent Itemsets and its Associated Data

Database is a repository of information. Retrieving automatic patterns from the database provide ... more Database is a repository of information. Retrieving automatic patterns from the database provide the requisite information and are in great demand in various domains of science and engineering. The effective pattern mining methods such as pattern discovery and association rule mining have been developed and find its applicability in a wide gamut ranging from science to medical to military and to engineering applications. Contemporary methods of retrieval such as pattern discovery and association rule mining algorithms are useful only for retrieving the data. The limitations of using these techniques are that they are unable to provide a complete association and relationship among the diverse patterns that is retrieved. This paper attempts a solution to the above limitation by designing a new algorithm (CFIM) which generates closed frequent patterns and its associated data concurrently. CFIM makes explicit the relationship between the patterns and its associated data.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Mining Bases for Association Rules using

We address the problem of the usefulness of the set of discovered association rules. This problem... more We address the problem of the usefulness of the set of discovered association rules. This problem is important since real-life databases yield most of the time several thousands of rules with high confidence. We propose new algorithms based on Galois closed sets to reduce the extraction to bases for exact and approximate rules. Once frequent closed itemsets -- which constitute a generating set for both frequent itemsets and association rules -- have been discovered, no additional database pass is needed to derive these bases. Experiments conducted on real-life databases show that these algorithms are e#cient and valuable in practice.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Closed sets based discovery of small covers for association rules

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Lakhal: Fast computation of concept lattices using data mining techniques

2 1 Technische Universit"at Darmstadt, Fachbereich Mathematik,

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Lakhal: Fast computation of concept lattices using data mining techniques

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Towards an object database approach for managing concept lattices

Proceedings of the 16th International Conference on Conceptual Modeling, 1997

ABSTRACT The concept lattice is a conceptual model firstly introduced by Wille in formal concept ... more ABSTRACT The concept lattice is a conceptual model firstly introduced by Wille in formal concept analysis, a theory of concept formation derived from lattice and order theory. Various concept lattice based applications have been reported in several domains such as conceptual clustering, conceptual knowledge representation and acquisition, and information retrieval. In this paper, we propose an object database approach for managing concept lattices in these applications. The goal of our work is two-fold. First, we extend the concept lattice model by basic operations supporting concept analysis. These operations allow to search and discover data directly from the concept lattice. Then, we present an approach for modeling and querying concept lattices within an object database framework.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Querying Concept Lattices in Object Databases

Iadt, 1998

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Computing iceberg concept lattices with T

Dke, 2002

... rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathema... more ... rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We ... 40]. 2. Formal concept analysis. Since ...

Bookmarks Related papers MentionsView impact

Research paper thumbnail of PASCAL : un algorithme d extraction des motifs fr�quents

Tsi, 2002

Bookmarks Related papers MentionsView impact