clustering algorithms are ubiquitous in modern data science pipelines, and are utilized in numerous fields ranging from biology to facility location. Due to their widespread use, especially in societal resource alloca...
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Extensive channel measurements have shown that multipath components (MPCs) generally exist as clusters, and cluster-based channel models are therefore widely used for system design and assessment. Since the dynamic be...
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Extensive channel measurements have shown that multipath components (MPCs) generally exist as clusters, and cluster-based channel models are therefore widely used for system design and assessment. Since the dynamic behavior, i.e., the time evolution, of the channels plays an important role for many applications, an accurate algorithm for the clustering of time-varying MPCs is required; a variety of algorithms have been proposed, yet little attention has been given to a fair comparison of their relative advantages and drawbacks. In this paper, we review and investigate the typical clustering methods for MPCs in wireless channels. Three popular classes of algorithms, namely distance-based (K-Power-Means), density-based (K-power-density), and evolution-based clustering methods, are analyzed and compared based on both synthetic and measured channel data. The F-measure is used to quantify and evaluate the clustering performance of the three algorithms, and also investigate their performance when only static snapshots of the channel are available. From the comparison, the evolution-based clustering method shows great potential to address the dynamic clustering problem for wireless time-varying channels.
An empirical study on three graph-based clustering algorithms has been presented here. We have discussed those three clustering algorithms and made an analysis. The algorithms we have considered here are: Markov Clust...
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ISBN:
(纸本)9781665426060
An empirical study on three graph-based clustering algorithms has been presented here. We have discussed those three clustering algorithms and made an analysis. The algorithms we have considered here are: Markov clustering (MCL), Regularized Markov clustering Algorithm (R-MCL), and Variable Inflation MCL (VI-MCL). We have used two types of graph networks: random network generated using synthetic data and PPI networks generated from 22 candidate genes of Malaria. We have considered the ubiquitous Dunn Index as the cluster validity index (CVI) to validate the generated clusters. Our experiments reveal that VI-MCL produces a lot of singletons set on both synthetic graph and PPI graph of those 22 genes. MCL performs best among the three algorithms we have chosen. The quality of clusters produces by R-MCL is better than that of VI-MCL.
University course timetabling is a NP-hard problem that be performed for each semester frequently. In this paper, we use a two-step algorithm for timetabling of common lecturers among departments. In the first step, w...
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University course timetabling is a NP-hard problem that be performed for each semester frequently. In this paper, we use a two-step algorithm for timetabling of common lecturers among departments. In the first step, we use a fuzzy multi-criteria decision-making comparison and local search algorithms with seven neighborhood structures and random iteration. It means that we use a fuzzy multi-criteria comparison algorithm to eliminate the ambiguities and soft constraints of common lecturers among departments. In addition, we apply the local search algorithm with seven neighboring structures to avoidtrapping intolocaloptima and improve the fuzzy multi-criteria comparison over the preferences and soft constraints of lecturers. In the second step, the common lecturers' timetable generated in the first step by the clustering approach (k-means, fuzzy c-means and funnel shape) is clustered based on the preferences and soft constraints of common lecturers among departments. Now, our common lecturers prepared by the clustering algorithms are mapped to the traversed free resources according to the paper's aims: (1) descending satisfaction of preferences and soft constraints of common lecturers among departments and (2) minimizing the loss of extra resources of each faculty, so that an optimal instance of our common lecturers timetabling is generated among departments. The applied datasets are in terms of satisfying the scheduling requirements in the real world for multi-departments of Islamic Azad University of Ahar branch.
Bag-of-Visual-Words is a technique used to create image vocabularies which describes the best image features. The construction of visual vocabulary is done using various clustering techniques. This work concentrates o...
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ISBN:
(纸本)9781665442121
Bag-of-Visual-Words is a technique used to create image vocabularies which describes the best image features. The construction of visual vocabulary is done using various clustering techniques. This work concentrates on various clustering techniques that are implemented on Bag-of-Visual-Words technique so that to analyse the accuracy of vocabulary creation. The clustering techniques such as K-means, Mini-batch K-means, Mean-shift, DBSCAN and OP-TICS are implemented individually to record the efficiency of the model. Features from the input images are extracted using Scale Invariant Feature Transform(SIFT) matched with Fast Library for Approximate Nearest Neighbors(FLANN). Drowsy images are classified based on the occurrence of the visual words. The comparison result indicates that the OPTICS clustering algorithm works well with Bag-of-Visual-Words to output an accuracy rate of 79.01%.
A Vehicular Ad hoc Network (VANET) is a kind of mobile ad hoc network (MANET), where the nodes in VANET are vehicles. VANET is the main component for the development of the Intelligent Transportation System (ITS). VAN...
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Background: Cluster analysis is a core task in modern data-centric computation. Algorithmic choice is driven by factors such as data size and heterogeneity, the similarity measures employed, and the type of clusters s...
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Background: Cluster analysis is a core task in modern data-centric computation. Algorithmic choice is driven by factors such as data size and heterogeneity, the similarity measures employed, and the type of clusters sought. Familiarity and mere preference often play a significant role as well. Comparisons between clustering algorithms tend to focus on cluster quality. Such comparisons are complicated by the fact that algorithms often have multiple settings that can affect the clusters produced. Such a setting may represent, for example, a preset variable, a parameter of interest, or various sorts of initial assignments. A question of interest then is this: to what degree do the clusters produced vary as setting values change? Results: This work introduces a new metric, termed simply "robustness", designed to answer that question. Robustness is an easily-interpretable measure of the propensity of a clustering algorithm to maintain output coherence over a range of settings. The robustness of eleven popular clustering algorithms is evaluated over some two dozen publicly available mRNA expression microarray datasets. Given their straightforwardness and predictability, hierarchical methods generally exhibited the highest robustness on most datasets. Of the more complex strategies, the paraclique algorithm yielded consistently higher robustness than other algorithms tested, approaching and even surpassing hierarchical methods on several datasets. Other techniques exhibited mixed robustness, with no clear distinction between them. Conclusions: Robustness provides a simple and intuitive measure of the stability and predictability of a clustering algorithm. It can be a useful tool to aid both in algorithm selection and in deciding how much effort to devote to parameter tuning.
Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms ...
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A method to extract the retina characteristic points for the purpose of medical diagnosis of the human eye is presented in this research. The proposed method helps to make the primary decision about the illness faster...
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A method to extract the retina characteristic points for the purpose of medical diagnosis of the human eye is presented in this research. The proposed method helps to make the primary decision about the illness faster and can be used on mobile devices. The algorithm is mostly based on the characteristic points (the so-called minutiae). These structures are commonly used in the biometric applications for fingerprint-based people recognition. In the case of the conducted research, this trait was used to differentiate healthy eyes from unhealthy ones. The methods were evaluated by appropriately implemented algorithms, showing promising results. Each solution was created with object-oriented programming language. The accuracy of the classification (healthy versus samples with pathological changes) was evaluated using four algorithms: k-Nearest Neighbors, k-Means and Support Vector Machines (SVM) with linear and third-degree polynomial as well as our own approach based on counting the minutiae number. Performance requirements were also checked, and it was verified that the computing power of modern mobile devices is sufficient to implement the proposed solution. The highest accuracy result was equal to 96,45% and was obtained with the third-degree polynomial SVM. This was a novel approach. For comparative purposes, we also implemented currently used solutions for image analysis - deep learning (DL) and Convolution Neural Networks (CNNs). Both medical and computer science backgrounds are presented in the work with the main methodology components to include image segmentation using the Gaussian Matched Filter, binarization by Local Entropy Thresholding and classification with the previously mentioned approaches.
The quality of marine statistical data is the life of marine statistical work, and it is the key to reflecting the level of marine statistical work, which is related to the credibility of marine statistical department...
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The quality of marine statistical data is the life of marine statistical work, and it is the key to reflecting the level of marine statistical work, which is related to the credibility of marine statistical departments. Therefore, in marine statistics work, we need to establish perfect marine statistical data quality evaluation criteria. Only by meeting the basic standards will we gradually establish a comprehensive and systematic marine statistical data quality monitoring system, which will guarantee the authority of the marine statistical data. This paper further improves the quality of marine statistical data in China by using a clustering algorithm.
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