Dist Frequent Next Neighbours: A Distributed Galois Lattice Algorithm for Frequent Closed Itemsets Extraction

The general purpose of this paper is to propose a distributed version of frequent closed itemsets extraction in the context of big data. The goal is to have good performances of frequent closed itemsets extraction as frequent closed item-sets are bases for frequent itemsets. To achieve this goal, we have extended the Galois lattice technique (or concept lattice) in this context. Indeed, Galois lattices are an efficient alternative for extracting closed itemsets which are interesting approaches for generating frequent itemsets. Thus we proposed Dist Frequent Next Neighbour which is a distributed version of the Frequent Next Neighbour concept lattice construction algorithm, which considerably reduces the extraction time by parallelizing the computation of frequent concepts (closed itemsets).

Systemic approach for optimizing information technology resource as a contribution of information system governance

<p> Information technology (IT) resource management is considered as one of the main pillars of Information System (IS) governance in the company. In this article, we propose a systemic approach from the structural paradigm based essentially on the formal extension of the ISO 19440 Meta-model. This structure contains specific constructs from the Cobit framework and system tools as the Galois lattices, likely to bring a better vision of the use of IT resource in the company. This technique allows a systemic analysis applied to special structural matrices to evaluate the deployment mode of IT resource to achieve business processes. Such approach allows a good optimization of IT resource as a pragmatic and effective contribution to Information System governance.</p>

Galois lattices and strongly divisible lattices in the unipotent case

Let

Visualizing and Interacting with Concept Hierarchies

Concept Hierarchies and Formal Concept Analysis (FCA) are theoretically well grounded. They rely on line diagrams called Galois lattices for visualizing and analysing object-attribute sets. Galois lattices are visually seducing and conceptually rich for experts. However they present important drawbacks due to their concept oriented overall structure: analysing what they show is difficult for non experts, navigation is cumbersome, interaction is poor, and scalability is a deep bottleneck for visual interpretation even for experts. In this paper we introduce semantic probes as a means to overcome many of these problems and extend usability and application possibilities of traditional FCA visualization methods. Semantic probes are visual user centred objects which extract and organize reduced Galois sub-hierarchies. They are simpler, clearer, and they provide a better navigation support through a rich set of interaction possibilities. Since probe driven sub-hierarchies are limited to users' focus, scalability is under control and interpretation is facilitated. After some successful experiments, several applications are being developed with the remaining problem of finding a compromise between simplicity and conceptual expressivity.

Constructing Galois Lattices as a Commonsense Reasoning Process

The concept of good classification test is used in this chapter as a dual element of the interconnected algebraic lattices. The operations of lattice generation take their interpretations in human mental acts. Inferring the chains of dual lattice elements ordered by the inclusion relation lies in the foundation of generating good classification tests. The concept of an inductive transition from one element of a chain to its nearest element in the lattice is determined. The special reasoning rules for realizing inductive transitions are formed. The concepts of admissible and essential values (objects) are introduced. Searching for admissible and essential values (objects) as a part of reasoning is based on the inductive diagnostic rules. Next, the chapter discusses the relations between constructing good tests and the Formal Concept Analysis (FCA). The decomposition of inferring good classification tests is advanced into two kinds of subtasks that are in accordance with human mental acts. This decomposition allows modeling incremental inductive-deductive inferences. The problems of creating an integrative inductive-deductive model of commonsense reasoning are discussed in the last section of this chapter.