Review on Fuzzy Clustering Algorithms

Image segmentation especially fuzzy-based segmentation techniques are widely used due to effective segmentation performance. For this reason, a number of algorithms are proposed in the literature. This paper presents a survey report of different types of classical fuzzy clustering techniques which are available in the literature

Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally. (C) 2019 Published by Elsevier Inc.Igor Škrjanc, Jose Antonio Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3

Evolving Clustering Algorithms And Their Application For Condition Monitoring, Diagnostics, & Prognostics

Applications of Condition-Based Maintenance (CBM) technology requires effective yet generic data driven methods capable of carrying out diagnostics and prognostics tasks without detailed domain knowledge and human intervention. Improved system availability, operational safety, and enhanced logistics and supply chain performance could be achieved, with the widespread deployment of CBM, at a lower cost level. This dissertation focuses on the development of a Mutual Information based Recursive Gustafson-Kessel-Like (MIRGKL) clustering algorithm which operates recursively to identify underlying model structure and parameters from stream type data. Inspired by the Evolving Gustafson-Kessel-like Clustering (eGKL) algorithm, we applied the notion of mutual information to the well-known Mahalanobis distance as the governing similarity measure throughout. This is also a special case of the Kullback-Leibler (KL) Divergence where between-cluster shape information (governed by the determinant and trace of the covariance matrix) is omitted and is only applicable in the case of normally distributed data. In the cluster assignment and consolidation process, we proposed the use of the Chi-square statistic with the provision of having different probability thresholds. Due to the symmetry and boundedness property brought in by the mutual information formulation, we have shown with real-world data that the algorithm’s performance becomes less sensitive to the same range of probability thresholds which makes system tuning a simpler task in practice. As a result, improvement demonstrated by the proposed algorithm has implications in improving generic data driven methods for diagnostics, prognostics, generic function approximations and knowledge extractions for stream type of data. The work in this dissertation demonstrates MIRGKL’s effectiveness in clustering and knowledge representation and shows promising results in diagnostics and prognostics applications

Clustering of nonstationary data streams: a survey of fuzzy partitional methods

YesData streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift.Ministero dell‘Istruzione, dell‘Universitá e della Ricerca

Detection and separation of generic-shaped objects by fuzzy clustering

Purpose - Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometrically-shaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily-shaped objects. Design/Methodology/Approach – With the aim of separating arbitrary shaped objects in an image, this paper presents a new detection and separation of generic shaped objects (FKG) algorithm that analytically integrates arbitrary shape information into a fuzzy clustering framework, by introducing a shape constraint that preserves the original object shape during iterative scaling. Findings - Both qualitative and numerical empirical results analysis corroborate the improved object segmentation performance achieved by the FKG strategy upon different image types and disparately shaped objects. Originality/Value - The proposed FKG algorithm can be highly used in the applications where object segmentation is necessary. Like this algorithm can be applied in MPEG-4 for real object segmentation that is already applied in synthetic object segmentation

Classification of fresh edible oils using a coated piezoelectric sensor array-based electronic nose with soft computing approach for pattern recognition

An electronic nose based on an array of six bulk acoustic wave polymer-coated piezoelectric quartz (PZQ) sensors with soft computing-based pattern recognition was used for the classi-fication of edible oils. The electronic nose was presented with 346 samples of fresh edible oil headspace volatiles, generated at 45°C. Extra virgin olive (EVO), nonvirgin olive oil (NVO) and sunflower oil (SFO) were used over a period of 30 days. The sensor responses were visualized by plotting the results from principal component analysis (PCA). Classification of edible oils was carried out using fuzzy c-means as well as radial basis function (RBF) neural networks both from a raw data and data after having been preprocessed by fuzzy c-means. The fuzzy c-means results were poor (74%) due to the different cluster sizes. The result of RBF with fuzzy c-means preprocessing was 95% and 99% for raw data input. RBF networks with fuzzy c-means preprocessing provide the advantage of a simple architecture that is quicker to train.</p