Dispersion Dynamics of Clustered Particles by Centrifugal Force

In this paper an approach is proposed to solve the problem of aggregation in nanomaterials through the mean of rotational separation aiming to quickly disperse clustered nanoparticles while not affecting their purity. If it is possible, this approach may replace the current mean of mechanical mixing, which may cause impurities issues. The hypothesis is that the centrifugal force due to rotational velocity acting on the nanoparticles can overcome the cohesive force between the nanoparticles, therefore dispersing the clustered nanoparticles. The experimental mean is to put different spheres connected by different types of glues imitating different nanoparticle clusters into centrifuges imitating the swivel plate. The results from both the theoretical model and the experiment show that for a cluster with a cohesive force of 1.75N, a rotational velocity of about 800rad./s is required to disperse the cluster. While for a cluster with a cohesive force of 0.25N and the same mass and position, a rotational velocity of about 150 rad./s is required to disperse the cluster. Except for the cohesive force, the mass and position of the nanoparticle on the swivel plate also have a large effect on the required rotational velocity. The observation of the physical mechanism of the dispersion has also shown that while using this way, the cluster is dispersed slowly with small parts separated from it. Therefore, this way can also eliminate re-clustering problems of nanoparticles.

Clustering Algorithm for Vehicle’s Driving Data Feature based on Integrated Navigation

The clustering result of K-means clustering algorithm is affected by the initial clustering center and the clustering result is not always global optimal. Therefore, the clustering analysis of vehicle’s driving data feature based on integrated navigation is carried out based on global K-means clustering algorithm. The vehicle mathematical model based on GPS/DR integrated navigation is constructed and the vehicle’s driving data based on GPS/DR integrated navigation, such as vehicle acceleration, are collected. After extracting the vehicle’s driving data features, the feature parameters of vehicle’s driving data are dimensionally reduced based on kernel principal component analysis to reduce the redundancy of feature parameters. The global K-means clustering algorithm converts clustering problem into a series of sub-cluster clustering problems. At the end of each iteration, an incremental method is used to select the next cluster of optimal initial centers. After determining the optimal clustering number, the feature clustering of vehicle’s driving data is completed. The experimental results show that the global K-means clustering algorithm has a clustering error of only 1.37% for vehicle’s driving data features and achieves high precision clustering for vehicle’s driving data features.

F-neuron in the clustering problems

We propose a new type of neuron based on the use of Fourier transform properties. This new type of neuron, called Fourier neuron (F-neuron), simplifies solving of a range of problems belonging to the class of problems of creating self-organizing networks using teacherless learning. The application of such F-neuron improves the quality and efficiency of automatic clustering of objects. We described the basic principles and approaches that allow to consider the properties vector as a parametric piecewise linear function, which provides the possibility to switch to Fourier-images operation both for input objects and for learning weights. The reasons for transferring information processing to Fourier space are justified, automatic orthogonalization and ranking of the Fourier image of the feature vector is explained. The advantages of the statistical approach to neuron training and construction of the refined neuron state function based on the parameters of the normal distribution are analyzed. We describe the procedure of training and pre-training the F-neuron that uses a statistical model based on the use of parameters of a normal distribution to calculate the confidence interval. We described an algorithm for recalculating normal distribution parameters when a new sample is added to the cluster. We reviewed some results of F-neuron technology and compared it with a traditional perceptron. A list of references and citations to the author’s previous works are given below.

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

On K-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts

The recovery efforts of the tourism and hospitality sector are compromised by the emergence of COVID-19 variants that can escape vaccines. Thus, maintaining non-pharmaceutical measures amidst massive vaccine rollouts is still relevant. The previous works which categorize tourist sites and restaurants according to the perceived degree of tourists' and customers’ exposure to COVID-19 are deemed relevant for sectoral recovery. Due to the subjectivity of predetermining categories, along with the failure of capturing vagueness and uncertainty in the evaluation process, this work explores the use k-means clustering with dataset values expressed as interval-valued intuitionistic fuzzy sets. In addition, the proposed method allows for the incorporation of criteria (or attribute) weights into the dataset, often not considered in traditional k-means clustering but relevant in clustering problems with attributes having varying priorities. Two previously reported case studies were analyzed to demonstrate the proposed approach, and comparative and sensitivity analyses were performed. Results show that the priorities of the criteria in evaluating tourist sites remain the same. However, in evaluating restaurants, customers put emphasis on the physical characteristics of the restaurants. The proposed approach assigns 12, 15, and eight sites to the “low exposure”, “moderate exposure”, and “high exposure” cluster, respectively, each with distinct characteristics. On the other hand, 16 restaurants are assigned “low exposure”, 16 to “moderate exposure”, and eight to “high exposure” clusters, also with distinct characteristics. The characteristics described in the clusters offer meaningful insights for sectoral recovery efforts. Findings also show that the proposed approach is robust to small parameter changes. Although idiosyncrasies exist in the results of both case studies, considering the characteristics of the resulting clusters, tourists or customers could evaluate any tourist site or restaurant according to their perceived exposure to COVID-19.

Develop a dynamic DBSCAN algorithm for solving initial parameter selection problem of the DBSCAN algorithm

<p>The amount of data has been increasing exponentially in every sector such as banking securities, healthcare, education, manufacturing, consumer-trade, transportation, and energy. Most of these data are noise, different in shapes, and outliers. In such cases, it is challenging to find the desired data clusters using conventional clustering algorithms. DBSCAN is a popular clustering algorithm which is widely used for noisy, arbitrary shape, and outlier data. However, its performance highly depends on the proper selection of cluster radius <em>(Eps)</em> and the minimum number of points <em>(MinPts)</em> that are required for forming clusters for the given dataset. In the case of real-world clustering problems, it is a difficult task to select the exact value of Eps and <em>(MinPts)</em> to perform the clustering on unknown datasets. To address these, this paper proposes a dynamic DBSCAN algorithm that calculates the suitable value for <em>(Eps)</em> and <em>(MinPts)</em> dynamically by which the clustering quality of the given problem will be increased. This paper evaluates the performance of the dynamic DBSCAN algorithm over seven challenging datasets. The experimental results confirm the effectiveness of the dynamic DBSCAN algorithm over the well-known clustering algorithms.</p>