PIFHC: The Probabilistic Intuitionistic Fuzzy Hierarchical Clustering Algorithm

Varshney, Ayush K.; Muhuri, Pranab K.; Lohani, Q. M. Danish

doi:10.1016/j.asoc.2022.108584

Cited by 20 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an alternative to conventional fuzzy sets, Atanassov ( 1986 ) formulated the conception of an intuitionistic fuzzy set that protrudes the conventional fuzzy set by consolidating a membership grade of negativity along with a membership grade of positivity within the interval [0, 1], which represents the appropriate strategy to recognize a way of human thinking (Naseem et al 2022 ; Dai et al 2023 ). The intuitionistic fuzzy theory becomes more resilient and prevalent in real-world issues like clustering analysis, decision analysis, location (site) selection, pattern identification, risk assessment, situation assessment, and supplier selection (Dai et al 2023 ; Jia et al 2023 ; Qiao and Wang 2023 ; Varshney et al 2022 ). Nonetheless, the intuitionistic fuzzy theory states that only those information duplets are permitted whose total membership grades of positivity and negativity range between [0, 1].…”

Section: Literature Review and Research Vacanciesmentioning

confidence: 99%

“…One of the long-established statistical measures for estimating linear relationships between quantitative objects is the notion of correlation coefficients (Riaz et al 2021 ; Varshney et al 2022 ; Wang and Chen 2022 ). There were several extended forms of correlation coefficients for the intensity of statistical relationships, depending on different kinds of equivocal and indistinct data in order to boost the usefulness of correlation coefficients within highly uncertain environments (Li et al 2022 ; Singh and Ganie 2022 ; Varshney et al 2022 ). However, identifying an appropriate correlation coefficient in the T-SF configuration is not trivial in complicated uncertain situations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple criteria decision analytic methods in management with T-spherical fuzzy information

Chen

2023

Artif Intell Rev

View full text Add to dashboard Cite

With a focus on T-spherical fuzzy (T-SF) sets, the aim of this paper is to create a split-new appraisal mechanism and an innovative decision analytic method for use with multiple-criteria assessment and selection in uncertain situations. The T-SF frame is the latest recent advancement in fuzzy settings and uses four facets (consisting of membership grades of positivity, neutrality, negativity, and refusal) to elucidate complex uncertainties, thereby evidently reducing information loss, in anticipation of fully manifesting indistinct and equivocal information. This paper adds to the body of knowledge regarding multiple criteria choice modeling by raising T-SF correlation-oriented measurements connected to the fixed and displaced ideal/anti-ideal benchmarks and by creating an approachable appraisal mechanism for advancing a T-SF decision analytic methodology. Consider, in particular, the performance ratings of available options in terms of judging criteria under the T-SF type of uncertainties. This research gives correlation-oriented measurements focusing on two varieties of maximum and square root functions in T-SF situations, which serve as a solid foundation for an efficacious appraisal mechanism from two views of anchored judgments corresponding to the fixed and displaced benchmarks. The T-SF Minkowski distance index is generated to integrate the outranking and outranked identifiers relying on correlation-oriented measurements for figuring out the local outranking and outranked indices. The T-SF decision analytic procedures are constructed using a new appraisal significance index that is founded on certain valuable insights of correlation-oriented maximizing and minimizing indices as well as global outranking and outranked indices. Additionally, a concrete location selection dilemma is dealt with in this research to showcase the applicability and efficiency of the suggested T-SF decision analytic methodology. Sensitivity analyses and comparative studies are carried out to investigate substantial modifications in pertinent parameters and to confirm the robustness of the predominance relationships among the available options. The suggested approaches are adaptable, flexible, and reliable, according to the application outcomes and comparison findings. This research provides four scientific contributions: (1) the utilization of T-SF correlation coefficients as the basis for prioritization analysis involving multiple criteria assessments, (2) the evolution of the T-SF Minkowski distance index to model outranking decision-making processes, (3) the creation of a reliable appraisal mechanism based on T-SF correlation-oriented measurements for intelligent decision support, and (4) the advancement of computational tools and procedures (e.g., correlation-oriented maximizing and minimizing indices, global outranking and outranked indices, and appraisal significance indices) to perform the decision analytic procedure in T-SF settings. In terms of managerial implications, the solution findings support the employment of...

show abstract

Section: Literature Review and Research Vacanciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple criteria decision analytic methods in management with T-spherical fuzzy information

Chen

2023

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…The partition-based clustering algorithm first defines the indicator function, but most of its algorithms are heuristic algorithms, which are difficult to deal with large-scale data types, easy to fall into a local minimum, and weak in noise processing [19,20]; In clustering algorithms, the segmentation between levels is highly dependent, and as long as the aggregation is performed, the results cannot be modified [21][22][23]; density-based clustering algorithms use the density function for clustering, and the nodes are continuously expanded according to the clustering, which can handle any data type, but is sensitive to custom user parameters [24][25][26]; the grid-based clustering algorithm is a discretised method to process spatial data, but the grid particles are not easy to control, and the parameter sensitivity is high [27,28]; the clustering algorithm based on neural network has some shortcomings, mainly due to the long training time [29,30].…”

Section: Overview Of Variable Clusteringmentioning

confidence: 99%

Research on the optimisation of logistics parcel intelligent sorting and conveying chain combined with variable clustering mathematical method

Yan

Huang

2022

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

The rapid development of China’s economy, especially the rapid rise of the logistics and distribution industry in recent years, coupled with the rise of e-commerce in recent years, has created a huge impact on the traditional logistics industry. Aiming at the sorting system of small and medium-sized logistics distribution centrer, this paper proposes an item allocation strategy based on customer demand in combination with practical application requirements and adopts the direct dynamic clustering algorithm based on hierarchical clustering. As a clustering index, all items in the distribution centre are clustered and the final clustering result is obtained. The results show that the maximum value of segmentation using the single connection method is 5.8, reflecting that the distribution distance is more advantageous; the maximum value of the segmentation method using the median method is 2.94, and the minimum value is 2.35, which reflects that the result of the algorithm is relatively uniform. A reasonable item allocation strategy has a certain positive effect and influence on the development of modern logistics and the service quality of the logistics industry.

show abstract

“…FCM is the most common way of introducing the membership function in fuzzy set theory into the calculation of distance to achieve better cluster division [33]. In recent years, scholars have not only improved fuzzy mean clustering on fuzzy computing rules [34][35][36] but also proposed a series of fuzzy clustering methods [37][38][39], such as Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) [40]. PIFHC considers intuitionistic fuzzy sets to deal with the uncertainty present in the data.…”

Section: Application Of Fuzzy Theorymentioning

confidence: 99%

Fuzzy MLKNN in Credit User Portrait

2022

View full text Add to dashboard Cite

Aiming at the problems of subjective enhancement caused by the discretization of credit data and the lack of a multi-dimensional portrait of credit users in the current credit data research, this paper proposes an improved Fuzzy MLKNN multi-label learning algorithm based on MLKNN. On the one hand, the subjectivity of credit data after discretization is weakened by introducing intuitionistic fuzzy numbers. On the other hand, the algorithm is improved by using the corresponding fuzzy Euclidean distance to realize the multi-label portrait of credit users. The experimental results show that Fuzzy MLKNN performs significantly better than MLKNN on credit data and has the most significant improvement on One Error.

show abstract

PIFHC: The Probabilistic Intuitionistic Fuzzy Hierarchical Clustering Algorithm

Cited by 20 publications

References 37 publications

Multiple criteria decision analytic methods in management with T-spherical fuzzy information

Multiple criteria decision analytic methods in management with T-spherical fuzzy information

Research on the optimisation of logistics parcel intelligent sorting and conveying chain combined with variable clustering mathematical method

Fuzzy MLKNN in Credit User Portrait

Contact Info

Product

Resources

About