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Clustering
The task of grou** data points or instances into clusters is quite fundamental in data science. In general, clustering methods belong to the area...
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Research on Scenario-Based Clustering Model and Analysis Method for Airworthiness Provisions
Airworthiness provisions are the requirements that must be followed in the development of civil aircraft. In view of the abstract characteristics of...
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Clustering Analysis of Spain at the Regional Level for the Life Insurance Sector
A clustering analysis of Spain, at the regional level, addressed to the life insurance sector, is displayed in this paper. Such analysis is based on...
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Novel similarity measure between hesitant fuzzy set and their applications in pattern recognition and clustering analysis
The extension of classical fuzzy sets are hesitant fuzzy sets (HFSs), in which each element has a possible value from [0,1]. Similarity and distance...
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Modeling and Analysis of Clustering by Medoids Using Uppaal
This paper describes an approach to formal modeling of clustering algorithms based on medoids using timed automata. The approach permits to assess...
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Clustering graph data: the roadmap to spectral techniques
Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal...
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Cluster Analysis and Comparative Study of Different Clustering Performance and Validity Indices
One of the most well-known problems in data mining is clustering. Clustering, an unsupervised classification technique, entails the identification of...
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Feature and Dimensionality Reduction for Clustering with Deep Learning
This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for...
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Performance Analysis of Hybridized Fuzzy Clustering Algorithms Using Metaheuristic Algorithms
Fuzzy clustering effectively handles the problem of mystically separable data through fuzzy partitioning. Popularly known as soft clustering, fuzzy...
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Performance Analysis of Clustering Using Modified Grey Wolf Optimization
Data clustering is the widely used technique in academia and industry to analyse large volumes of data with unknown patterns. Data clustering...
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Clustering
Supervised algorithms use labeled data as an input for develo** a prediction model. However, the amount of unlabeled data collected often far...
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Quantitative Analysis Methods of Clustering Techniques
The hard and soft clustering techniques have membership functions. The chief objective of these functions is converging the final solution at the...
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Application of Data Mining Clustering for Patterns Analysis of Cyberbullying Surveys
In latest years, harassment or abuse through mobile devices and the Internet has been on the rise. This issue, better known as cyberbullying, is...
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Novel distance measures of hesitant fuzzy sets and their applications in clustering analysis
Distance and similarity measures are very important in clustering, pattern recognition, decision-making and other scientific fields. For the existing...
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Clustering of Building Stock
In Europe, buildings account for 40% of final energy demand. Building stock models assess the impacts of technologies on energy consumption,...
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Construction of Data Mining Model of CRM Marketing Based on Big Data Clustering Analysis
In the current competitive business landscape, the significance of Customer Relationship Management (CRM) has become increasingly prominent, as...
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Quantum density peak clustering
Clustering algorithms are of fundamental importance when dealing with large unstructured datasets and discovering new patterns and correlations...
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Clustering and Analysis of Rural E-commerce Live Broadcast Mode Based on Data Orientation
The rapid development of network technology has revolutionized information dissemination and made it possible for various new communication channels...
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A survey on deep clustering: from the prior perspective
Facilitated by the powerful feature extraction ability of neural networks, deep clustering has achieved great success in analyzing high-dimensional...
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Clustering and Thematic Analysis of News Content Using Machine-Learning Algorithms and Knowledge Graph
The amount of information on the Internet is increasing, and there is an increasing need for personalized content. Previously, only “quantity” was...