Discovering Ordinal Attributes Through Gradual Patterns, Morphological Filters and Rank Discrimination Measures

Abstract

This paper proposes to exploit heterogeneous data, i.e. data described by both numerical and categorical features, so as to gain knowledge about the categorical attributes from the numerical ones. More precisely, it aims at discovering whether, according to a given data set, based on information provided by the numerical attributes, some categorical attributes actually are ordinal ones and, additionally, at establishing ranking relations between the category values. To that aim, the paper proposes the 3-step methodology OSACA, standing for Order Seeking Algorithm for Categorical Attributes: it first consists in extracting gradual patterns from the numerical attributes, to identify rich ranking information about the data; it then applies mathematical morphology tools, more precisely alternated filters, to induce an associated order on the categorical attributes. The third step evaluates the quality of the candidate rankings through measures derived from the rank entropy discrimination.

An efficient hybrid by partitioning approach for extractingmaximal gradual patterns in large databases (MPSGrite)

“…The gradual support of a gradual pattern M is equal to the length of a maximal path associated with M divided by the entire number of datasets in the so-called maximum pathway approach (9,11,14,15). In this method, we have…”

An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite)

“…A progressive pattern's steady support In the so-called maximum pathways technique, M is equal to the length of a maximal path associated with M divided by the total number of datasets [9,11,14,15]. In this method, we have:…”

“…All tests on the datasets presented in the preceding part were performed on an Intel Core T M i7-2630QM CPU running on 2.00GHz x 8 with 8GB main memory & Ubuntu 16.04 LTS. We investigated a number of support levels for each dataset & measured the associated execution times (shown in Figures 3,5,8,10,12,14,16,& 18), as well as the number of retrieved patterns (shown in Figures 4,6,9,11,13,15,17,& 19). In these figures, (N It.…”