Data Mining with Neuro-Fuzzy Models

Klose, Aljoscha; Nürnberger, Andreas; Nauck, Detlef; Kruse, Rudolf

doi:10.1007/978-3-7908-1825-3_1

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…They obtained similar results and declared that k-means works good enough if considering the simplicity of k-means. The work of Jaewon et al [6] using a set of 230 large realworld social networks in SNAP. Although their research did a comparison by methods using SNAP data,they did not use any small-sized, anonymized data such as SNAP Facebook dataset.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

Sharma¹,

Sharma²,

Dwivedi³

2019

IJEAT

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People tend to build and maintain their friendship relying on SNS nowadays. Thus, the problem of how to organize the social network accurately and automatically. In this paper, a hybrid neuro-fuzzy approach is used. . Many aspect impact the error values like input/output, membership functions, the training data arrays, and the number of epochs needed to train the model. This paper is based on hybrid Neuro-Fuzzy concept for testing the link prediction for facebook data. We use Matlab to calculate average testing Error, View Generation Rule, OutputSurface.

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Section: Related Workmentioning

confidence: 99%

Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

Sharma¹,

Sharma²,

Dwivedi³

2019

IJEAT

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“…(11,1)] or [and (12, 3)] or [and (21,1)]or [and (22, 2)]} (8) Moreover, for the sake of inducing compact rule base a formal approach to manipulation of rule bases is suggested by performing operations on Boolean matrices and binary relations [62]. Because Boolean transformations of rule base can lead to more compact representations, Klose and Nurnberger presented an approach to building more expressive rules by performing Boolean transforming during and after learning from data [103].…”

Section: Rule Structures Of Fuzzy Rule-based Systemsmentioning

confidence: 99%

Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

Zhou¹,

Gan²

2008

Fuzzy Sets and Systems

236

140

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“…The most recent implementation of the NEFCLASS approach has been extended by a number of features that makes it more appropriate for real world applications. Those features include the treatment of missing values, the ability to use data with numeric and symbolic attributes and automatic pruning strategies to get more compact and readable rule bases [23,24].…”

Section: Supervised Extraction Of Fuzzy Rulesmentioning

confidence: 99%

Extracting fuzzy classification rules from partially labeled data

Klose

2004

Soft Computing - A Fusion of Foundations, Methodologies and App

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The interpretability and flexibility of fuzzy ifthen rules make them a popular basis for classifiers. It is common to extract them from a database of examples. However, the data available in many practical applications are often unlabeled, and must be labeled manually by the user or by expensive analyses. The idea of semi-supervised learning is to use as much labeled data as available and try to additionally exploit the information in the unlabeled data. In this paper we describe an approach to learn fuzzy classification rules from partially labeled datasets.

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Data Mining with Neuro-Fuzzy Models

Cited by 12 publications

References 15 publications

Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

Hybrid Neuro-Fuzzy Classification Algorithm for Social Network

Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

Extracting fuzzy classification rules from partially labeled data

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