New validity index for fuzzy-possibilistic c-means clustering.
problem Conflicting results in determining the optimal number of clusters due to noisy data points and outliers.
method Introducing a new validity index (FP index) for fuzzy-possibilistic c-means clustering.
result FP index works well in datasets with varying cluster shapes and densities.
A novel weighted distance improves fuzzy c-means clustering accuracy.
problem Improving fuzzy c-means clustering performance with weighted distances.
method Proposed Canberra Weighted Distance to enhance FCM algorithm.
result Experimental results show superior performance of the proposed method.
New fuzzy clustering method for distribution-valued data using adaptive Wasserstein distances.
problem Clustering distribution-valued data with adaptive weights.
method Fuzzy c-means algorithms using adaptive L2 Wasserstein distances. result Adaptive distances improve clustering of distribution-valued data.
New algorithm for fuzzy clustering improves clustering quality.
problem Seeding iterative fuzzy algorithms for high-quality clustering.
method MaxMin Linear initialization algorithm for fuzzy C-Means.
result Validation through experiments on various datasets.
CAF-HFCM automatically forms a cluster hierarchy and optimizes the number of clusters without trial-and-validation.
problem Challenges in determining the optimal number of clusters in fuzzy c-means.
method CAF-HFCM, an auto-fused hierarchical fuzzy c-means method.
result Automatic agglomeration and optimal number of clusters without validity indices.
This paper uses fuzzy C-Means clustering and sonification to analyze heart rate variability.
problem Identifying suitable features from HRV analysis for sonification.
method Unsupervised machine learning (fuzzy C-Means clustering) and sonification techniques.
result Improves sonification interpretability by selecting appropriate HRV features.
Paper compares two possibilistic segmentation methods for SAS imagery.
problem Segmenting synthetic aperture sonar images into different seafloor environments.
method Comparison of Possibilistic Fuzzy Local Information C-Means (PFLICM) and Possibilistic K-Nearest Neighbors (PKNN) algorithms.
result PKNN outperforms PFLICM in segmentation performance on SAS images.
A new hybrid fuzzy-crisp clustering algorithm addresses imbalanced cluster sizes.
problem Imbalanced influence in fuzzy c-means clustering for large vs. small clusters.
method Hybrid fuzzy-crisp algorithm combining linear and quadratic membership functions, setting exact zero membership for sufficiently distant points.
result The hybrid algorithm outperforms conventional fuzzy and crisp clustering methods on imbalanced datasets.
Robust clustering methods for multivariate time series data.
problem Clustering multivariate time series data robustly to outliers.
method Quantile-based fuzzy C-means with metric, noise, and trimmed approaches.
result Robust methods outperform alternatives in handling outlying series.
A novel fuzzy clustering method for multivariate time series.
problem Clustering multivariate time series with varying dependencies and dynamics.
method Quantile-based cross-spectral features, PCA, fuzzy C-means, fuzzy C-medoids.
result Substantially outperforms existing methods in various evaluation schemes.
FCM clustering adapts to persistence diagrams for topological data analysis.
problem Integrating topological data into machine learning workflows.
method Adapting Fuzzy c-Means to persistence diagrams.
result FCM clustering captures topological structure without additional processing.
A new fuzzy clustering method using hyperbolic smoothing for large datasets.
problem Building fuzzy clusters for large data sets efficiently.
method A novel smoothing numerical approach to relax the sum-of-squares criterion, converting the problem into a differentiable optimization problem.
result The method produces better fuzzy partitions compared to traditional fuzzy C-means. The paper revisits PCM and APCM clustering algorithms from an uncertainty perspective.
problem Uncertainty in clustering algorithms and its impact on clustering behavior.
method Introducing parameters σ_v and α to characterize uncertainty, modeling membership values as conditional fuzzy sets.
result Unified clustering framework (UPCM) that reduces to PCM and APCM under specific conditions.
Proposes PKM for soft K-means clustering.
problem Soft K-means (m=1) unsolved since 1981.
method Probabilistic K-Means (PKM) via nonlinear programming.
result Proposed methods solve PKM efficiently.
New cluster validity index detects optimal number of clusters and secondary options.
problem Determining the optimal number of clusters in fuzzy clustering.
method Correlation-based fuzzy cluster validity index (WP index) using fuzzy c-means algorithm.
result WP index outperforms existing indexes in detecting optimal number of clusters and secondary options.
Kernelized SUSAN fuzzy clustering improves noisy image segmentation.
problem Inability of existing algorithms to preserve edge information and structural details.
method Kernelized Weighted SUSAN fuzzy C-Means clustering with spatial constraints and edge quality metric.
result Improved segmentation of noisy images with preserved edge information and structural details.
SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.
problem Lack of supervised clustering for TSK fuzzy classifiers.
method SESSC integrates within-cluster compactness, between-cluster separation, and label information.
result SESSC initialization outperforms other clustering methods, especially for small rule numbers.
Proposes ITISC for clustering with minimized worst-case expected distortions.
problem Real-world clustering data distribution mismatch.
method Information theoretical importance sampling, constrained minimax optimization, Lagrange method.
result Validation of ITISC on synthetic and real-world datasets.
Proposes MinimaxFCM for multi-view clustering of data from multiple sources.
problem Clustering data from multiple heterogeneous views.
method Minimax optimization-based fuzzy c means clustering.
result MinimaxFCM outperforms other multi-view clustering methods.
New visual quality index for fuzzy clustering.
problem No accurate quality index for fuzzy clustering across datasets.
method Proposes a new visual quality index and graph-based solution.
result Validated through extensive experiments on various datasets.
A new clustering method learns shared hidden space and fuzzy partition between multi-view data.
problem Effective exploitation of relationship between different views in multi-view data.
method Hidden space sharing multi-view fuzzy clustering (HSS-MVFC) method based on fuzzy c-means.
result The proposed method outperforms many related clustering methods in experiments.
SFP extends fuzzy clustering for supervised tasks using labels and entropy regularization.
problem Applying fuzzy clustering to supervised tasks without sacrificing performance.
method Generative model incorporating labels through a surrogate term and entropy regularization.
result SFP outperforms state-of-the-art algorithms on various datasets.
Paper uses ANFIS to predict cryptocurrency prices.
problem Predicting cryptocurrency prices for seven days.
method Adaptive Network Based Fuzzy Inference System (ANFIS) with hybrid and backpropagation algorithms.
result The method can predict cryptocurrency prices in a short time.
This paper compares spike sorting techniques for rat brain neuronal activity.
problem Improving the accuracy of spike sorting for neuronal activity analysis.
method Three-step spike sorting process: detection, feature extraction, and clustering. Various methods are compared.
result Kernel PCA outperforms in feature extraction, leading to better spike sorting results.
Develops a new fuzzy model using QPs and ewl2 regularization to improve local region behavior.
problem Inability of constant and linear functions to accurately describe local regions in fuzzy models.
method Applied Fuzzy C-Means for structure identification, used QPs as consequents, introduced ewl2 regularization.
result Improved model's ability to describe local regions without overfitting.
Proposes a fuzzy rule-based method for data visualization.
problem Preserving neighborhood relationships and handling non-linear manifolds in data visualization.
method Uses a first-order Takagi-Sugeno model with clusters and Geodesic c-means clustering for rule generation and parameter estimation.
result Behaves desirably and performs better than or comparable to other methods.
Wavelet features improve image clustering and segmentation accuracy.
problem Noise and lack of spatial context in pixel intensity-based methods.
method Modified K-means, Fuzzy c-means, and ACWE algorithms incorporating Wavelet features.
result Wavelet-based algorithms converge to different segmentation results based on frequency information.
Estimates statistical power for cluster analysis in biomedical research.
problem Lack of established methods to compute a priori statistical power for cluster analysis.
method Simulation studies varying subgroup size, number, separation, and covariance structure.
result Sufficient statistical power achieved with small samples (N=20-30) for large effect sizes.
Paper simulates LR fuzzy intervals with interval-valued cores.
problem Generating random fuzzy intervals with interval-valued cores.
method Developed algorithms for simulating LR fuzzy numbers with interval-valued cores.
result Numerically efficient algorithm for simulating fuzzy values.
In this paper, we have tried to apply the concepts of fuzzy sets to Lie groups and its relative concepts. First, we define a C1 fuzzy submanifold after reviewing C1−fuzzy manifold definition. In main section, we defined the Lie group and some its relative concepts such as fuzzy transformation group,…
A new clustering algorithm REFCMFS improves K-Means efficiency and robustness.
problem Efficiently clustering data with outliers and L0-norm constraints. method REFCMFS uses L2,1-norm robust loss and L0-norm constraint on membership matrix. result REFCMFS achieves more promising performance and efficient optimization.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
DCFS predicts stock indices using deep learning and fuzzy systems.
problem Predicting stock indices with high-dimensional inputs.
method Bottom-up layer-by-layer design of DCFS, parameter sharing.
result DCFS models outperform weak estimators and real-world data.
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
problem Expanding and reducing feature space in tabular learning becomes computationally expensive with increased dimensionality.
method SCOPE-FE controls the search space by regulating operator and feature-pair spaces, using OperatorProbing and FeatureClustering.
result SCOPE-FE reduces feature engineering time while maintaining competitive predictive performance.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
Paper defines kernels for fuzzy sets, useful in machine learning.
problem Uncertainty in data with fuzzy set membership.
method Defined cross product, intersection, non-singleton, and distance-based kernels.
result Kernels improve machine learning tasks with fuzzy data.
Study predicts heart failure patient survival using stacked ensemble ML.
problem Predicting survival of heart failure patients.
method Collect and analyze patient data, apply SMOTE, use K-Means, Fuzzy C-Means clustering, Random Forest, XGBoost, Decision Tree, and propose a stacked ensemble model.
result Supervised ML algorithms outperform unsupervised models, achieving high accuracy and F1 score.
MBGD-RDA trains TSK fuzzy systems efficiently on big datasets.
problem Training TSK fuzzy systems efficiently on big datasets.
method MBGD-RDA combines mini-batch gradient descent, regularization, AdaBound, and novel techniques for TSK fuzzy systems.
result MBGD-RDA achieves fast convergence and superior generalization on big datasets.
Enhanced fuzzy system predicts chaotic time series with improved accuracy.
problem Forecasting chaotic time series with high uncertainty.
method Combines evolving fuzzy systems, participatory learning, KRLS, and type-2 fuzzy sets.
result Proposed model outperforms other methods in accuracy and complexity.
A new adaptive binarization technique using fuzzy integrals improves image quality.
problem Improving image thresholding quality.
method FLAT (Fuzzy Local Adaptive Thresholding) based on fuzzy integrals.
result The proposed FLAT method produces better image quality than traditional algorithms and neural networks.
In most gene expression data, the number of training samples is very small compared to the large number of genes involved in the experiments. However, among the large amount of genes, only a small fraction is effective for performing a certain task. Furthermore, a small subset of genes is desirable in developing gene e…
Paper investigates differentiable fuzzy implications and their suitability for learning.
problem Analyzing differentiable fuzzy implications and their suitability for learning.
method Investigates the properties of fuzzy implications in a differentiable setting and introduces a new family of fuzzy implications.
result Various fuzzy implications are unsuitable for differentiable learning, and a new family of fuzzy implications is introduced.
Fuzzy hashes learn from data to improve file similarity detection.
problem Measuring similarity between files, especially malware.
method Learned fuzzy hashes using a minimax training framework.
result Learned fuzzy hashes outperform traditional methods for file similarity.
This paper explores fuzzy systems' equivalence to neural networks and other machine learning methods.
problem Designing optimal fuzzy systems and overcoming challenges.
method Comparative analysis of Takagi-Sugeno-Kang fuzzy systems with neural networks, mixture of experts, CART, and stacking ensemble regression.
result Functional equivalence between fuzzy systems and machine learning methods.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
New method reduces complexity of fuzzy decision trees in Big Data.
problem Reducing complexity in multi-way fuzzy decision trees for Big Data classification.
method Two-step process: 1) Probability integral transform, 2) Ruspini strong fuzzy partition.
result Up to 6 million fewer leaves with similar classification accuracy.
Fuzzy ROC handles indeterminate data points in ROC analysis.
problem Handling indeterminate data points in ROC analysis.
method Extends ROC visualization to indeterminate data, addressing sensitivity and specificity bounds.
result Visual summarization of possibilities from different indeterminacy zones.
Unified probabilistic foundation for fuzzy simplicial sets in dimensionality reduction.
problem Lack of clear probabilistic interpretation in fuzzy simplicial sets.
method Introducing a probabilistic framework explaining fuzzy simplicial sets as marginals of probability measures on simplicial sets.
result Unified probabilistic theoretical foundation for fuzzy simplicial sets.