Four matroidal structures of covering and their relationships with rough sets

Resolution enhancement for lung 4D-CT based on transversal structures by using multiple Gaussian process regression learning

Graph theory, as an important approach in data mining, can be applied to dimensionality reduction. As illustrated here, this paper proposes a new graph-theory method that reduces data dimensionality in a more effective and efficient manner than traditional methods. The proposed method, namely related family, is based on a hypergraph information system. The method not only compute all reducts of dimension set, but also adopts a heuristic algorithm to get one dimensionality reduction. The proposed heuristic algorithm can achieve more noisy-tolerable results in a low time complexity.

Dimensionality Reduction of Hypergraph Information System

&lt;p&gt;&lt;strong&gt;A common problem often encountered in sanitary napkin packaging is inadequate product protection. Failure to open or reseal the packaging and the use of materials that cannot be reused are complaints from 97% of consumers regarding sanitary napkin products currently on the market. This research aims to analyze packaging design elements according to consumer preferences. In this research, the Kansei Engineering and Rough Set methods were used. Kansei Engineering has the advantage of being able to translate consumer emotions into design elements. The design element analysis process is carried out using the Rough Set method. The research results obtained consumer emotions in the form of 19 Kansei words and 29 packaging samples as design references. Analysis of sanitary napkin packaging design elements obtained recommendations in the form of cardboard material, folding cardboard packaging type, no additional features, box body, opening type with flap, medium size, feminine design, script typography, and image elements in the form of pictures and illustrations. This research succeeded in proving Kansei Engineering's ability to produce packaging designs according to consumer preferences through quantitative data analysis&lt;em&gt;.&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt; - &lt;em&gt;Model Kano, Rough Set, Design Element, Packaging&lt;/em&gt;&lt;/p&gt; 

pdf/analisis-elemen-kemasan-sekunder-pembalut-dengan-metode-3u1o7i5zpt.pdf

Analisis Elemen Kemasan Sekunder Pembalut Dengan Metode Rough Set

Neighborhood rough set (NRS) is usually only applicable to small datasets due to the large number of useless and repetitive neighborhood calculations, which severely limits the efficiency of NRS. Many studies improve the efficiency of NRS by narrowing the neighborhood search range down and achieve good performance on small datasets, but they do not perform well on big datasets. To further improve the efficiency on big datasets, we propose a fast attribute reduction method for big datasets based on NRS (FARforBD). In addition, a theorem is also represented to prove the correctness and effectiveness of the proposed method. In FARforBD, we further reduce the neighborhood search range to a neighborhood without any positive region samples. This method greatly reduces many useless neighborhood calculations. The comparison experiments on big datasets show the effectiveness and efficiency of FARforBD. 

/pdf/fast-attribute-reduction-for-big-datasets-based-on-2lg5ign2.pdf

Fast Attribute Reduction for Big Datasets Based on Neighborhood Rough Set

The granular reduction is to delete dispensable elements from a covering. 
It is an efficient method to reduce granular structures and get rid of the
redundant information from information systems. In this paper, we develop
an algorithm based on discernability matrixes to compute all the granular
reducts of covering rough sets. Moreover, a discernibility matrix is simplified
to the minimal format. In addition, a heuristic algorithm is proposed as well
such that a granular reduct is generated rapidly.

/pdf/a-granular-reduction-algorithm-based-on-covering-rough-sets-4f1t6tgl14.pdf

A Granular Reduction Algorithm Based on Covering Rough Sets

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A Granular Reduction Algorithm Based on Covering Rough Sets