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Soft Computing: Three Decades Fuzzy Models and Applications
The paper attempts to give protection of soft computing in the investigations of the scientist form the Institutes of Informatics, Information...
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An Approach to Fault Diagnosis Using Fuzzy Clustering Techniques
In this paper a novel approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented. In the proposal,...
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Interval Type-2 Fuzzy Possibilistic C-Means Clustering Algorithm
In this paper, we present the extension of the fuzzy possibilistic C-means (FPCM) algorithm using type-2 fuzzy logic techniques, with the goal of...
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A Proposal of On-Line Detection of New Faults and Automatic Learning in Fault Diagnosis
In this paper a new approach of automatic learning for a fault diagnosis system using fuzzy clustering techniques is presented. The proposal presents...
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A Comparative Analysis of Various Image Segmentation Techniques
In this ascension era of technology, Magnetic resonance imaging (MRI) emerges as the utmost clinically acceptable imaging modality for detection and...
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Customer Segmentation by Various Clustering Approaches and Building an Effective Hybrid Learning System on Churn Prediction Dataset
Success of every organization or firm depends on Customer Preservation (CP) and Customer Correlation Management (CCM). These are the two parameters...
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Fuzzy weighted c-harmonic regressions clustering algorithm
As a well-known regression clustering algorithm, fuzzy c -regressions (FCR) has been widely studied and applied in various areas. However, FCR appears...
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Algorithms of Combinatorial Cluster Analysis
While Chap. 2 presented the broadness of the spectrum of clustering methods, this chapter focusses...
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Machine and Statistical Learning
Databases and big data are used for constructing models to have a better understanding of the data, or to make decisions. Machine and statistical...
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Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization
Fuzzy c-means (FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means (PFCM) clustering...
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An Efficient Kernelized Fuzzy Possibilistic C-Means for High-Dimensional Data Clustering
Clustering high-dimensional data has been a major concern owing to the intrinsic sparsity of the data points. Several recent research results signify...
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A Review of Soft Classification Approaches on Satellite Image and Accuracy Assessment
Classification is a widely used technique for image processing and is used to extract thematic data for preparing maps in remote sensing...
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A Generalization of Rand and Jaccard Indices with Its Fuzzy Extension
The Jaccard and Rand indices are the best-known and used similarity measures. In general, the Jaccard index is relatively conservative, but the Rand...
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Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method
Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in...
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Possibilistic C-means Algorithm Based on Collaborative Optimization
In this paper, a new possibilistic C-means (PCM) clustering algorithm is proposed based on particle swarm optimization (PSO) and simulated annealing...
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Fuzzy Statistical Decision-Making
The classification of decision-making methods can be based on the types of the data in hand. If the data are given as a decision matrix with discrete...
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Mammogram Image Segmentation Using Hybridization of Fuzzy Clustering and Optimization Algorithms
Mammogram images have the ability to assist physicians in detecting breast cancer caused by cells abnormal growth. But due to visual interpretation,...
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On Cluster Extraction from Relational Data Using Entropy Based Relational Crisp Possibilistic Clustering
The relational clustering is one of the clustering methods for relational data. The membership grade of each datum to each cluster is calculated...
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Possibilistic biclustering algorithm for discovering value-coherent overlap** δ-biclusters
One of the important tools for analyzing gene expression data is biclustering method. It focuses on finding a subset of genes and a subset of...
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Overview of Overlap** Partitional Clustering Methods
Identifying non-disjoint clusters is an important issue in clustering referred to as Overlap** Clustering. While traditional clustering methods...