A study of cluster hypergraphs and its properties

Maity, Ananta; Das, Kousik; Samanta, Sovan; Mondal, Sukumar; Dubey, Vivek

doi:10.1007/s13278-021-00721-7

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The dataset is made into ten clusters [46][47][48][49][50][51] using the k-means clustering algorithm, without any amalgamation of another algorithm as depicted in Fig. 2.…”

Section: Resultsmentioning

confidence: 99%

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Karthik¹,

Bhadoria²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.

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“…The dataset is made into ten clusters [46][47][48][49][50][51] using the k-means clustering algorithm, without any amalgamation of another algorithm as depicted in Fig. 2.…”

Section: Resultsmentioning

confidence: 99%

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Karthik¹,

Bhadoria²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…The approach discussed in [30][31][32][33][34][35] is only considered a set of positive information. But our approach is considered positive information as well as at the same time negative information.…”

Section: Introductionmentioning

confidence: 99%

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Dhanalakshmi¹,

Samanta²,

Sivaraman³

et al. 2023

Computer Systems Science and Engineering

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Fuzzy sets have undergone several expansions and generalisations in the literature, including Atanasov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets, to name a few. They can be regarded as fuzzy multisets from a formal standpoint; nevertheless, their interpretation differs from the two other approaches to fuzzy multisets that are currently available. Hesitating fuzzy sets (HFS) are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships. However, these possible memberships can be not only crisp values in [0,1], but also interval values during a practical evaluation process. Hesitant bipolar valued fuzzy set (HBVFS) is a generalization of HFS. This paper aims to introduce a general framework of multi-attribute group decision-making using social network. We propose two types of decision-making processes: Type-1 decision-making process and Type-2 decision-making process. In the Type-1 decision-making process, the experts' original opinion is proces for the final ranking of alternatives. In Type-2 decision making processs, there are two major aspects we consider. First, consistency tests and checking of consensus models are given for detecting that the judgments are logically rational. Otherwise, the framework demands (partial) decision-makers to review their assessments. Second, the coherence and consensus of several HBVFSs are established for final ranking of alternatives. The proposed framework is clarified by an example of software packages selection of a university.

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Bunch graph based dimensionality reduction using auto-encoder for character recognition

Bhadoria

Samanta²,

Pathak

et al. 2022

Multimed Tools Appl

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A study of cluster hypergraphs and its properties

Cited by 3 publications

References 17 publications

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Bunch graph based dimensionality reduction using auto-encoder for character recognition

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