A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation

Shi, Zhongchao; Chen, Shiping

doi:10.1155/2018/1838639

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Data penelitian ini di ambil dari Tima indian Dateset yang meliputi 768 kasus diabetes yang valid dari 3925 kasus daibetes dan digunakan dalam penelitian ini. Hasilnya Rought set dan knowledge granulation membantu memahami dan mengklasifikasikan knowledge base sehingga baik untuk pembuatan keputusan serta efektif mencegah adanya pengulangan value (Redudancy) [12]. Penelitian selanjutnya dilakauna oleh Tifanny dan melda pada tahun 2018 untuk penentuan persediaan obat-obatan di RSUP Adam Malik Medan.…”

Section: Pendahuluanunclassified

Penentuan Tingkat Kerusakan Peralatan Labor Komputer Menggunakan Data Mining Rough Set

Salam¹,

Defit²

2019

JSisfotek

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Equitments of computer laboratory have a function as an important tools in supporting pratical lecturing. These facilitiesshould always be on a condition like ready are proper to use both computers and others. To avoid equipment detriment, it is necessary to do early identification in which prevent the worse condition of equitments. The method use in this study is roughset method wich consists several stages such as Decision System, Equivalence Class, Discernibility Matrix, DiscernibilityMatrix Modulo D, Reduction, and Generate Rules. From this study, it was found that 14 rules in making decisions for equipments treatment of computer laboratory such as use, repair and replace. Thus, this mrthod is very capable indetermining the detriment level of laboratory equipment

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Section: Pendahuluanunclassified

Penentuan Tingkat Kerusakan Peralatan Labor Komputer Menggunakan Data Mining Rough Set

Salam¹,

Defit²

2019

JSisfotek

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“…The granularity was described using the dimension of weight through the threshold structure. Zhenquan [23] proposed rough set theory and granular theory to determine the weight of the attributes to define their effectiveness from the nonredundant aspect and redundant aspects. Li [24] experimented with the fundamental geographical data for formal concept analysis and hence obtained the feasible result of the complex geo ontological methods.…”

Section: Introductionmentioning

confidence: 99%

Attributes Reduction on SE-ISI Concept Lattice for an Incomplete Context Using Object Ranking

Srirekha¹,

Sathish²,

Devi

et al. 2023

Mathematics

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The formal concept of lattice plays a vital role in knowledge discovery. Reduction of the attribute has many applications in machine learning technology and data mining fields. In this paper, we introduce an object ranking concept to define a consistency set and the reduction of the attributes by structural features. An incomplete information system works on the three-way concepts using the SE-ISI Context. The granular was emphasized with join (meet) irreducible sets using the object ranking concepts. A dual operator is defined based on the object ranking concepts and its properties and conditions are verified. Hence, this elaborates on the four kinds of reduction of the attributes. The ordered pairs give the knowledge of the attributes that deal with the interval set of both the approximation of rough set theory concerning the objects. Therefore, the relationship between four kinds of reduction of the attribute was appropriate to access the consistency set using the object ranking concepts by some of the theorems and examples.

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“…They also calculated the weight for each attribute using the degree of importance in rough set theory and entropy information. Shi and Chen [14] proposed a new method on rough set theory and granular theory, characterizing the effectiveness of weight. Li et al [15] experimented with a combination of fundamental geographic information data and proposed numerous applications for geoontologies merging.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Method for Attribute Reduction: Weighted Attribute Concepts for Probabilistic Analysis of Decision Attributes

Baskaran,

Sathish

2023

MMEP

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Attribute reduction, a seminal aspect of data analysis, primarily hinges on the indiscernibility matrix. Previous studies have explored the weight of an attribute via various methods, yet achieving optimal reduction remains elusive. This study proposes a novel approach to optimal reduction, leveraging the concept of weighted attributes based on the probability values of core and non-core elements. This approach scrutinizes the accuracy of both core and non-core attributes, thereby enhancing our comprehension of the object's attributes. The weighted attribute concept is derived in light of entropy information and the indiscernibility matrix. A discernibility matrix aids in ascertaining the reduct, whereas entropy information facilitates the analysis of the weight of uncertain data. By deploying decision attributes, we derive the core and its corresponding probabilistic value, establishing an algebraic structure as an ordered pair of objects with associated weight concepts. This structure further enables the investigation of the consistency set and the join (meet) irreducible set employing weighted attribute concepts. Ultimately, optimal reduction is determined by the weight of non-core elements, allowing a comprehensive analysis of the information system and procurement of its essential attributes for decision-making. The proposed concept of weighted attributes is elucidated using a biological dataset.

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A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation

Cited by 4 publications

References 27 publications

Penentuan Tingkat Kerusakan Peralatan Labor Komputer Menggunakan Data Mining Rough Set

Penentuan Tingkat Kerusakan Peralatan Labor Komputer Menggunakan Data Mining Rough Set

Attributes Reduction on SE-ISI Concept Lattice for an Incomplete Context Using Object Ranking

An Innovative Method for Attribute Reduction: Weighted Attribute Concepts for Probabilistic Analysis of Decision Attributes

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