Fuzzy clustering
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Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering Open
As fuzzy c-means clustering (FCM) algorithm is \nsensitive to noise, local spatial information is often introduced \nto an objective function to improve the robustness of the FCM \nalgorithm for image segmentation. However, the introductio…
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Multi-view clustering: A survey Open
In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining a…
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Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering Open
Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several exte…
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Fuzzy System Based Medical Image Processing for Brain Disease Prediction Open
The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the…
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Doubly Aligned Incomplete Multi-view Clustering Open
Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Su…
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Clustering Approach Based on Mini Batch Kmeans for Intrusion Detection System Over Big Data Open
Intrusion detection system (IDS) provides an important basis for the network defense. Due to the development of the cloud computing and social network, massive amounts of data are generated, which inevitably brings much pressure to IDS. An…
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Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering Open
Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplet…
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DSets-DBSCAN: A Parameter-Free Clustering Algorithm Open
Clustering image pixels is an important image segmentation technique. While a large amount of clustering algorithms have been published and some of them generate impressive clustering results, their performance often depends heavily on use…
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A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots Open
With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time …
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BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation Open
Metagenomics has become an integral part of defining microbial diversity in various environments. Many ecosystems have characteristically low biomass and few cultured representatives. Linking potential metabolisms to phylogeny in environme…
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Hierarchical Clustering Open
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing …
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Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm Open
In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for de…
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Automatic Fuzzy Clustering Framework for Image Segmentation Open
Clustering algorithms by minimizing an objective function share a clear drawback of having to set the number of clusters manually. Although density peak clustering is able to find the number of clusters, it suffers from memory overflow whe…
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Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density Open
Automatic identification systems (AISs) serve as a complement to radar systems, and they have been installed and widely used onboard ships to identify targets and improve navigational safety based on a very high-frequency data communicatio…
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Fuzzy Clustering Algorithm for Enhancing Reliability and Network Lifetime of Wireless Sensor Networks Open
To prolong the function of wireless sensor networks (WSNs), the lifetime of the system has to be increased. WSNs lifetime can be calculated by using a few generic parameters, such as the time until the death of the first node and other par…
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Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making Open
The paper addresses the growing importance of Large Scale Group Decision Making (LSGDM) problems, focusing on hesitant fuzzy LSGDM. It introduces a Reliability Index-based Consensus Reaching Process (RI-CRP) to enhance efficiency. The prop…
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Unified Tensor Framework for Incomplete Multi-view Clustering and Missing-view Inferring Open
In this paper, we propose a novel method, referred to as incomplete multi-view tensor spectral clustering with missing-view inferring (IMVTSC-MVI) to address the challenging multi-view clustering problem with missing views. Different from …
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A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications Open
The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processi…
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Performance Analysis of Various Fuzzy Clustering Algorithms: A Review Open
Fuzzy clustering is useful clustering technique which partitions the data set in fuzzy partitions and this technique is applicable in many technical applications like crime hot spot detection, tissue differentiation in medical images, soft…
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A Feature-Reduction Multi-View k-Means Clustering Algorithm Open
The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas. Since internet, social network, and big data grow r…
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Clustering ensemble method Open
A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm wi…
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An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm Open
The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of …
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Variable selection methods for model-based clustering Open
Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to d…
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Spatiotemporal Data Clustering: A Survey of Methods Open
Large quantities of spatiotemporal (ST) data can be easily collected from various domains such as transportation, social media analysis, crime analysis, and human mobility analysis. The development of ST data analysis methods can uncover p…
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Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering Open
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation ind…
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Clustering Techniques and the Similarity Measures used in Clustering: A Survey Open
Clustering is an unsupervised learning technique which aims at grouping a set of objects into clusters so that objects in the same clusters should be similar as possible, whereas objects in one cluster should be as dissimilar as possible f…
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Higher-order clustering in networks Open
A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a t…
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Data Clustering: Algorithms and Its Applications Open
Data is useless if information or knowledge that can \nbe used for further reasoning cannot be inferred from it. \nCluster analysis, based on some criteria, shares data into important, practical or both categories (clusters) based on share…
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An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement Open
The advent of architectures such as the Internet of Things (IoT) has led to the dramatic growth of data and the production of big data. Managing this often-unlabeled data is a big challenge for the real world. Hierarchical Clustering (HC) …
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Analysis of Euclidean Distance and Manhattan Distance in the K-Means Algorithm for Variations Number of Centroid K Open
K-Means is a clustering algorithm based on a partition where the data only entered into one K cluster, the algorithm determines the number group in the beginning and defines the K centroid. The initial determination of the cluster center i…