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ردیف | عنوان | نوع |
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1 |
Exponential operational laws and new aggregation operators for intuitionistic multiplicative set in multiple-attribute group decision making process
قوانین عملیاتی نمایی و اپراتورهای تجمیع جدید برای مجموعه چند برابر شهودی در فرایند تصمیم گیری گروهی چند صفت-2020 The intuitionistic multiplicative preference set is one of the replacements to the intuitionistic
fuzzy preference set, where the preferences related to the object is asymmetrical distribution
about 1. In it, Saaty’s 1–9 scale has been used to represent the uncertain and
imprecise information. Meanwhile, an aggregation operator by using general operational
laws for some fuzzy sets is an important task to aggregate the different numbers.
Motivated by these primary characteristics, it is interesting to present the concept of exponential
operational laws, which differs from the traditional laws by the way, in which bases
are real numbers while exponents are the intuitionistic multiplicative numbers. In this
paper, we develop a methodto solve the Multiple Attribute Group Decision Making
(MAGDM) problem under the Intuitionistic Multiplicative Sets (IMS) environment. To do
it, firstly, we define some new exponential operational laws and a score function for IMS
and studied their properties. Secondly, based on this, we develop some averaging and geometric
aggregation operators and characterize their various properties. Thirdly, a novel
approach is promoted to solve MAGDM problems with IMS information. Finally, some
numerical illustrations are given with a comparative study to verify the approach. Keywords: Intuitionistic multiplicative sets | MAGDM | Exponential operational laws | Aggregation operators | Score function |
مقاله انگلیسی |
2 |
Combining hierarchical clustering approaches using the PCA method
ترکیب روشهای خوشه بندی سلسله مراتبی با استفاده از روش PCA-2019 In expert systems, data mining methods are algorithms that simulate humans’ problem-solving capabil- ities. Clustering methods as unsupervised machine learning methods are crucial approaches to catego- rize similar samples in the same categories. The use of different clustering algorithms to a given dataset produces clusters with different qualities. Hence, many researchers have applied clustering combination methods to reduce the risk of choosing an inappropriate clustering algorithm. In these methods, the out- puts of several clustering algorithms are combined. In these research works, the input hierarchical clus- terings are transformed to descriptor matrices and their combination is achieved by aggregating their descriptor matrices. In previous works, only element-wise aggregation operators have been used and the relation between the elements of each descriptor matrix has been ignored. However, the value of each element of the descriptor matrix is meaningful in comparison with its other elements. The current study proposes a novel method of combining hierarchical clustering approaches based on principle component analysis (PCA). PCA as an aggregator allows considering all elements of the descriptor matrices. In the proposed approach, basic clusters are made and transformed to descriptor matrices. Then, a final ma- trix is extracted from the descriptor matrices using PCA. Next, a final dendrogram is constructed from the matrix that is used to summarize the results of the diverse clustering. The experimental results on popular available datasets show the superiority of the clustering accuracy of the proposed method over basic clustering methods such as single, average and centroid linkage and previously combined hierar- chical clustering methods. In addition, statistical tests show that the proposed method significantly out- performed hierarchical clustering combination methods with element-wise averaging operators in almost all tested datasets. Several experiments have also been conducted which confirm the robustness of the proposed method for its parameter setting. Keywords: Clustering | Hierarchical clustering | Principle component analysis | PCA |
مقاله انگلیسی |
3 |
GimmeHop: A recommender system for mobile devices using ontology reasoners and fuzzy logic
GimmeHop: یک سیستم توصیه گر برای دستگاه های همراه سیار با استفاده از استدلال هستی شناسی و منطق فازی-2019 This paper describes GimmeHop, a beer recommender system for Android mobile devices using fuzzy ontologies to represent the relevant knowledge and semantic reasoners to infer implicit knowledge. GimmeHop use fuzzy quantifiers to deal with incomplete data, fuzzy hedges to deal with the user context, and aggregation operators to manage user preferences. The results of our evaluation measure empirically the data traffic and the running time in the case of remote reasoning, the size of the ontologies that can be locally dealt with in a mobile device in the case of local reasoning, and the quality of the automatically computed linguistic values supported in the user queries Keywords:Fuzzy ontologies | Aggregation | Fuzzy quantifiers | Recommender systems |
مقاله انگلیسی |
4 |
On constructing the largest and smallest uninorms on bounded lattices
ساخت بزرگترین و کوچکترین ناآرامی ها بر روی مشبک های محدود-2019 Uninorms on the unit interval are a common extension of triangular norms (t-norms) and triangular conorms (t-conorms). As important
aggregation operators, uninorms play a very important role in fuzzy logic and expert systems. Recently, several researchers
have studied constructions of uninorms on more general bounded lattices. In particular, Çaylı (2019) gave two methods for constructing
uninorms on a bounded lattice L with e ∈ L {0, 1}, which is based on a t-norm Te on [0, e] and a t-conorms Se on [e, 1]
that satisfy strict boundary conditions. In this paper, we propose two new methods for constructing uninorms on bounded lattices.
Our constructed uninorms are indeed the largest and the smallest among all uninorms on L that have the same restrictions Te and
Se on [0, e] and, respectively, [e, 1]. Moreover, our constructions does not require the boundary condition, and thus completely
solved an open problem raised by Çaylı. Keywords: Bounded lattices | Aggregation operators | Uninorms | Neutral elements |
مقاله انگلیسی |
5 |
Uncertain induced aggregation operators and its application in tourism management
عدم تعریف اپراتورهای تجمعی نامشخص و کاربرد آن در مدیریت گردشگری-2012 We develop a new decision making approach for dealing with uncertain information and apply it in tour
ism management. We use a new aggregation operator that uses the uncertain weighted average (UWA)
and the uncertain induced ordered weighted averaging (UIOWA) operator in the same formulation. We
call it the uncertain induced ordered weighted averaging – weighted averaging (UIOWAWA) operator.
We study some of the main advantages and properties of the new aggregation such as the uncertain
arithmetic UIOWA (UA-UIOWA) and the uncertain arithmetic UWA (UAUWA). We study its applicability
in a multi-person decision making problem concerning the selection of holiday trips. We see that depend
ing on the particular type of UIOWAWA operator used, the results may lead to different decisions.
Keywords: Interval numbers | Weighted average | OWA operator | Aggregation operators | Tourism management | Multi-person decision-making |
مقاله انگلیسی |