38,558 research outputs found
Revisiting Numerical Pattern Mining with Formal Concept Analysis
In this paper, we investigate the problem of mining numerical data in the
framework of Formal Concept Analysis. The usual way is to use a scaling
procedure --transforming numerical attributes into binary ones-- leading either
to a loss of information or of efficiency, in particular w.r.t. the volume of
extracted patterns. By contrast, we propose to directly work on numerical data
in a more precise and efficient way, and we prove it. For that, the notions of
closed patterns, generators and equivalent classes are revisited in the
numerical context. Moreover, two original algorithms are proposed and used in
an evaluation involving real-world data, showing the predominance of the
present approach
Interpretations of Association Rules by Granular Computing
We present interpretations for association rules. We first introduce Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). We then use extended random sets to present a new algorithm of finding interesting rules. We prove that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. We also provide two measures for dealing with uncertainties in association rules
An Investigation into the relationship between the gender binary and occupational discrimination using the implicit relational assessment procedure
The social construction of gender-as-binary plays an important role within many contemporary theories of gender inequality. However, to date, the field of psychology has struggled with the operationalization and assessment of binarist ideologies. The current article proposes a technical framework for the analysis of the gender binary and assesses the suitability of the Implicit Relational Assessment Procedure (IRAP) as a measure of binarist gender beliefs. Forty-seven undergraduate students (26 female; M-age = 23.84) completed two IRAPs assessing the coordination of certain traits exclusively with women and others exclusively with men. Effects found on the IRAP were in the expected direction (i.e., relating men but not women with certain traits and women but not men with other traits). In addition, the traits ascribed to men within the IRAP were evaluated as more hirable by a large majority of participants (83%) on an explicit preference task. The results therefore support the arguments that, first, gender traits do seem to be framed oppositionally in language and, second, this binary may underpin existing gender hierarchies in certain contexts
A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system
The advancement of technology had encouraged mankind to design and create useful
equipment and devices. These equipment enable users to fully utilize them in various
applications. Pulp mill is one of the heavy industries that consumes large amount of
electricity in its production. Due to this, any malfunction of the equipment might
cause mass losses to the company. In particular, the breakdown of the generator
would cause other generators to be overloaded. In the meantime, the subsequence
loads will be shed until the generators are sufficient to provide the power to other
loads. Once the fault had been fixed, the load shedding scheme can be deactivated.
Thus, load shedding scheme is the best way in handling such condition. Selected load
will be shed under this scheme in order to protect the generators from being
damaged. Multi Criteria Decision Making (MCDM) can be applied in determination
of the load shedding scheme in the electric power system. In this thesis two methods
which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by
Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis,
a series of analyses are conducted and the results are determined. Among these two
methods which are AHP and TOPSIS, the results shown that TOPSIS is the best
Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill
system. TOPSIS is the most effective solution because of the highest percentage
effectiveness of load shedding between these two methods. The results of the AHP
and TOPSIS analysis to the pulp mill system are very promising
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