Shadi Al Shehabi

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The main area of this paper concerns the neural methods for mapping scientific and technical information (articles, patents) and for assisting a user in carrying out the complex process of analysing large quantities of such information. In the procedure of information analysis, like in the domain of patent analysis, the complexity of the studied topics and(More)
Feature maximization is an alternative measure, as compared to usual distributional measures relying on entropy or on Chi-square metric or vector-based measures, like Euclidean distance or correlation distance. One of the key advantages of this measure is that it is operational in an incremental mode both on clustering and on traditional classification. In(More)
This paper presents a first attempt for performing a precise and automatic identification of the linking behaviour in a scientific domain through the analysis of the communication of the related academic institutions on the web. The proposed approach is based on the paradigm of multiple viewpoint data analysis (MVDA) than can be fruitfully exploited to(More)
This paper presents a numerical association rule extraction method that is based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. The proposed method copes with the usual problems of the symbolic association rule extraction method that are(More)