Hafida Belbachir

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—Recently, many large organizations have multiple data sources (MDS') distributed over different branches of an interstate company. Local patterns analysis has become an effective strategy for MDS mining in national and international organizations. It consists of mining different datasets in order to obtain frequent patterns, which are forwarded to a(More)
This paper presents the semantic analysis of queries written in natural language (French) and dedicated to the object oriented data bases. The studied queries include one or two nominal groups (NG) articulating around a verb. A NG consists of one or several keywords (application dependent noun or value). Simple semantic filters are defined for identifying(More)
Associative classification algorithms have been successfully used to construct classification systems. The major strength of such techniques is that they are able to use the most accurate rules among an exhaustive list of class-association rules. This explains their good performance in general, but to the detriment of an expensive computing cost, inherited(More)
Currently, geographic information systems (GIS) are accessible to a large public, in particular to users who cannot be considered as experts. However, the access to geographic information and its overtaking by various users are not easy. Indeed, between the rigidity of the available access languages and the ignorance of the organization of information(More)
Dans les sciences de l'information géographique les champs continus sont utilisés pour modéliser et représenter des phénomènes continus naturels et environnementaux. Ils sont d'un grand intérêt pour décrire la distribution de plusieurs propriétés physiques qui varient continuellement dans l'espace et dans le temps, comme la météorologie. Notre article(More)
Apriori Algorithms are used on very large data sets with high dimensionality. Therefore parallel computing can be applied for mining of association rules. The process of association rule mining consists of finding frequent item sets and generating rules from the frequent item sets. Finding frequent itemsets is more expensive in terms of CPU power and(More)
Data mining has become an important technology to discover a hidden and nontrivial knowledge from large amounts of data. A major problem is to achieve this discovery process with preserving privacy of extracted data and / or knowledge. Privacy preserving data mining (PPDM) is a new area of research that studies the side effects of knowledge mining methods(More)