Based on the ranking methodology of PROMETHEE, a new sorting method (ℱlow
ort) is proposed for assigning actions to completely ordered categories defined either by limiting profiles or by central ...profiles. The ℱlow
ort assignment rules are based on the relative position of an action with respect to the reference profiles, in terms of the incoming, leaving, and/or net flows. For a better understanding of the issues involved, a graphical representation is given. An explicit relationship between the assignments obtained when working either with limiting or central profiles is formalized. Finally, an empirical comparison with Electre-Tri is made to compare the resulting assignments.
Frequent Item-set Mining (FIM), sometimes called Market Basket Analysis (MBA) or Association Rule Learning (ARL), are Machine Learning (ML) methods for creating rules from datasets of transactions of ...items. Most methods identify items likely to appear together in a transaction based on the support (i.e. a minimum number of relative co-occurrence of the items) for that hypothesis. Although this is a good indicator to measure the relevance of the assumption that these items are likely to appear together, the phenomenon of very frequent items, referred to as ubiquitous items, is not addressed in most algorithms. Ubiquitous items have the same entropy as infrequent items, and not contributing significantly to the knowledge. On the other hand, they have strong effect on the performance of the algorithms and sometimes preventing the convergence of the FIM algorithms and thus the provision of meaningful results. This paper discusses the phenomenon of ubiquitous items and demonstrates how ignoring these has a dramatic effect on the computation performances but with a low and controlled effect on the significance of the results.
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