A novel soft set approach in selecting clustering attribute

Qin, Hongwu; Ma, Xiuqin; Zain, Jasni Mohamad; Herawan, Tutut

doi:10.1016/j.knosys.2012.06.001

Cited by 60 publications

(19 citation statements)

References 29 publications

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“…Soft set based decision-making was initiated by [3]. Further applications of soft sets in decision-making contexts were given, for example, in [24,36,37].…”

Section: Definition 1 ([2]mentioning

confidence: 99%

Valuation Fuzzy Soft Sets: A Flexible Fuzzy Soft Set Based Decision Making Procedure for the Valuation of Assets

2017

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Zadeh's fuzzy set theory for imprecise or vague data has been followed by other successful models, inclusive of Molodtsov's soft set theory and hybrid models like fuzzy soft sets. Their success has been backed up by applications to many branches like engineering, medicine, or finance. In continuation of this effort, the purpose of this paper is to put forward a versatile methodology for the valuation of goods, particularly the assessment of real state properties. In order to reach this target, we develop the concept of (partial) valuation fuzzy soft set and introduce the novel problem of data filling in partial valuation fuzzy soft sets. The use of fuzzy soft sets allows us to quantify the qualitative attributes involved in an assessment context. As a result, we illustrate the effectiveness and validity of our valuation methodology with a real case study that uses data from the Spanish real estate market. The main contribution of this paper is the implementation of a novel methodology, which allows us to assess a large variety of assets where data are heterogeneous. Our technique permits to avoid the appraiser's subjectivity (exhibited by practitioners in housing valuation) and the well-known disadvantages of some alternative methods (such as linear multiple regression).

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“…Soft set based decision-making was initiated by [3]. Further applications of soft sets in decision-making contexts were given, for example, in [24,36,37].…”

Section: Definition 1 ([2]mentioning

confidence: 99%

Valuation Fuzzy Soft Sets: A Flexible Fuzzy Soft Set Based Decision Making Procedure for the Valuation of Assets

2017

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“…Feng and Li [4] provided an extensive study of several types of soft subsets and soft equal relations. The fundamental reference for soft set based decision making draws on Maji, Biswas and Roy [6], but other extensions and applications of soft sets can be found in e.g., [3,9,10].…”

Section: Definition 1 (Molodtsov [8]) a Pair (Fmentioning

confidence: 99%

Incomplete Soft Sets: New Solutions for Decision Making Problems

Alcantud

Santos‐García

2016

Advances in Intelligent Systems and Computing

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Alcantud and Santos-García [2] revisit the soft set based decision making problem under incomplete information. Their solution relies on a classical Laplacian argument from probability theory. In view of the computational characteristics of such algorithm, we propose two related solutions that efficiently evaluate problems with many more incomplete data. A computational analysis assesses the performance of our algorithms and compares them with earlier solutions in the literature.

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“…It is inconvenient to execute these operations directly on the information system. Thus, Qin et al [19] constructed a soft set model over equivalence classes to facilitate the computation.…”

Section: A Soft Set Model On Equivalence Classmentioning

confidence: 99%

“…Meantime, define mapping . The pair is a soft set model over equivalence classes (NSS) [19]. Table 1 shows the tabular representation of , where and is either 1 or 0, , .…”

Section: A Soft Set Model On Equivalence Classmentioning

confidence: 99%

“…It has been proved that every rough set is a soft set, which shows the necessity to combine two theories together [18]. As for the dependency degree in the rough set theory, Qin et al [19] had found an efficient way to calculate it by constructing a soft set model (NSS) for an information system. Later, Mamat et al [20] gave a new clustering method of attributes based on the same soft set model.…”

Section: Introductionmentioning

confidence: 99%

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