Combined Clustering Algorithm Research Articles

Clustering for Clarity: Improving Word Sense Disambiguation through Multilevel Analysis

In natural language processing, a critical activity known as word sense disambiguation (WSD) seeks to ascertain the precise meaning of an ambiguous wordin context. Traditional methods for WSD frequently involve supervised learning methods and lexical databases like WordNet. However, these methods fallshort in managing word meaning complexity and capturing fine-grained differences. In this paper, for increasing the precision and granularity of word sensedisambiguation we proposed multilevel clustering method that goes deeper in the nested levels as locate groups of linked context words and categorize themaccording to their word meanings. With this method, we can more effectively manage polysemy and homonymy as well as detect minute differences in meaning. An actual investigation of the SemCor corpus demonstrates the performance score of multilevel clustering in WSD. This proposed method successfullyseparated clusters and groups context terms according to how semantically related they are, producing improved disambiguation outcomes. A more detailedknowledge of word senses and their relationships may be obtained thanks to the clustering process, which makes it possible to identify smaller clusters inside larger clusters. The outcomes demonstrate how multilevel clustering may enhance the granularity and accuracy of WSD. Our solution overcomes the drawbacks of conventional approaches and provides a more fine-grained representation of word senses by combining clustering algorithms.

A multi-objective site selection of electric vehicle charging station based on NSGA-II

The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

The application of integrating comprehensive evaluation and clustering algorithms weighted by maximal information coefficient for urban flood susceptibility

Different sub-regions of Zhengzhou city have various levels of sensitivity to flood due to the impact of urbanization. Thus, an accurate flood sensitivities assessment is a key tool for flood prevention and urban planning and development. To successfully link the urban flood sensitivity assessment with the real flood situation, a method combining clustering algorithm with comprehensive evaluation is presented. The proposed method is not affected by the classification standard of sensitivities levels and has a small and undemanding demand for flood data. First, Maximal Information Coefficient between conditional factors and flood is employed to determine the weight. Then, the different results are obtained by three clustering algorithms. Finally, a four-layer evaluation structure weighted by analytic hierarchy process is established to select the best flood susceptibility map. A case study in the Zhengzhou city, China shows that the positive scale amplification strategy is relatively best and the flood sensitivity of sub-regions in Zhengzhou city should be divided into four levels obtained by K-Means clustering. Hence, it supplies the valuable insights for the urban planning and flood mitigation.

Assessing Ships’ Environmental Performance Using Machine Learning

Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships’ environmental performance-related data, collected during ships’ voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships’ environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster.

Tillage-Depth Verification Based on Machine Learning Algorithms

In an analysis of the penetration resistance and tillage depth of post-tillage soil, four surface-layer discrimination methods, specifically, three machine learning algorithms—Kmeans, DBSCAN, and GMM—and a curve-fitting method, were used to analyze data collected from the cultivated and uncultivated layers. Among them, the three machine learning algorithms found the boundary between the tilled and untilled layers by analyzing which data points belonged to which layer to determine the depth of the soil in the tilled layer. The curve-fitting method interpreted the intersection among data from the fitted curves of the ploughed layer and the un-ploughed layer as the tillage depth. The three machine learning algorithms were used to process a standard data set for model evaluation. DBSCAN’s discrimination accuracy of this data set reached 0.9890 and its F1 score reached 0.9934, which were superior to those of the other two algorithms. Under standard experimental conditions, the ability of DBSCAN clustering to determine the soil depth was the best among the four discrimination methods, and the discrimination accuracy reached 90.63% when the error was 15 mm. During field-test verification, the discriminative effect of DBSCAN clustering was still the best among the four methods. However, the soil blocks encountered in the field test affected the test data, resulting in large errors in the processing results. Therefore, the combined RANSCA robust regression and DBSCAN clustering algorithm, which can eliminate interference from soil blocks in the cultivated layer and can solve the problem of large depth errors caused by soil blocks in the field, was used to process the data. After testing, when the RANSCA and DBSCAN combined method was used to process all samples in the field and the error was less than 20mm, the accuracy rate reached 82.69%. This combined method improves the applicability of discrimination methods and provides a new method of determining soil depth.

Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm.

A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The massive genetic datasets are complex. Gene expression datasets are enriched with useful genes that are associated with specific diseases such as cancer. In this study, we used two gene expression datasets from the Gene Expression Omnibus and preprocessed them before classification. We used optimal kernel principal component analysis in which the optimal kernel function was chosen for dataset dimensionality reduction and extraction of the most important features. The gene sets with a high validity index were collected using a combined hieratical clustering and optimal kernel principal component analysis (KHC-RLR) algorithm. Logistic regression is one of the most common methods for classification, and it has been shown to be a useful classification approach for gene expression data analysis. In this study, we used multi-class logistic regression to classify the collected gene sets. We found that ordinary logistic regression caused a major overfitting problem; therefore, we used regularized multi-class logistic regression to classify the gene sets. The proposed KHC-RLR algorithm showed a high performance and satisfied accuracy measures.

Classification of Online Course Teaching Cases Based on an Improved Clustering Method

The classification and sharing of online course teaching cases based on students’ individual learning needs can help teachers save time and cost in preparation of lessons and enrich their teaching content, and what is more, it is in line with the digitalization trend of the modern data-driven education. Currently, there has been few research on the combination of new information technology with teaching case classification, and the research is not adequate on the existing algorithms for classification of online course teaching cases. Therefore, this paper takes the teaching cases of online open courses of finance as the object, and studies the classification method for online course teaching cases based on an improved clustering method. First, the text clustering problem of finance teaching cases was described, and the conversion process of the abstracts of the case text and the evaluation method for the importance of words to teaching cases were given. Then, the key sentences were extracted from the text of the finance teaching cases, and the related algorithm was introduced. After that, the clustering flow chart for long texts of finance teaching cases was shown, and the principle of the combined clustering algorithm for finance teaching cases with uneven distribution of long texts and short texts was described in detail. The experimental results verified the effectiveness of the proposed algorithm.

Theoretical investigation of the stability of A55-nBn nanoalloys (A, B = Al, Cu, Zn, Ag)

Nanoalloys have been investigated for a wide range of applications, however, our atomistic understanding of the physical-chemistry properties is still far from complete as quantum-size effects play an important role for particles with about 1nm diameter. In this work, we employed density functional theory calculations to investigate the structural, energetic and electronic properties of 55-atom A55-nBn nanoalloys, where A, B= Al, Cu, Zn, and Ag. For structure generation, we combined clustering algorithm techniques with design principles, which yields a wide range of conformations. From the analyses of the excess energy results, we found that the CuAl, CuAg, CuZn, and AlAg systems are the most energetically favorable, in particular, the Al42Cu13 and Al42Ag13 compositions (onion-like and core–shell structures, respectively). Through Spearman’s correlation analysis, we found that the structural properties (number of under coordinated atoms, effective coordination number, average bond lengths, and chemical order parameter) are the most important descriptors correlated with the energy stability of the nanoalloys (excess energy). Several properties such as the particle volume, binding energy, and average bond length show a linear dependence as a function of composition.

Autocorrelation Ratio as a Measure of Inertia for the Classification of Extreme Events

One of the problems in modern hydrology concerns the estimation of design events at sites with scarce or null data. This is a challenge for developing countries because they do not have monitoring networks as extensive and reliable as in developed nations. This situation has caused states in the Latin American and Caribbean region, in particular, to rely on hydrological regionalization techniques. These procedures implement clustering algorithms in combination with aggregation rules and metric distances to generate homogeneous groups from which hydrological information can be transferred. In addition, it has been proven that the analysis of spatial variables is sensitive to the magnitudes of extreme events; therefore, a mathematical formulation that adopts this fact into consideration must be included. For this purpose, the autocorrelation distance of the daily rainfall data series is proposed as an estimator of temporal variability. The fit parameters of the mixed Poisson-exponential probability distribution are operated as estimators of spatial variability. These spatio-temporal conditions are combined to obtain a mathematical relation of the autocorrelation as a measure of inertia for the classification of extreme events. This procedure is applied to Hydrologic Region 10 in northwestern Mexico from daily rainfall records. This zone has already been explored in terms of its regional homogeneity, which allows validating the results obtained.

A Combined Clustering Algorithm Based on ESynC Algorithm and a Merging Judgement Process of Micro-Clusters

The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clusters or micro-clusters, then merges those conjoint micro-clusters by using a merging judgement process. For some datasets that ESynC and SynC cannot detect correct clusters, CESynC can capture natural clusters. From the simulation experiments, we observe that CESynC can get better (or the same) clustering results than (or as) that of ESynC in many cases. We also observe that the clustering results of CESynC and ESynC are often better than that of SynC. Therefore, we can say CESynC can often obtain better clustering quality than ESynC and SynC in some kinds of datasets. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of CESynC.

An Unsupervised Model for Identifying and Characterizing Dark Web Forums

Dark Web forums are significantly exploited to trade confidential information and illicit products by criminals. This paper addresses the problem of how to identify the cluster of discussion forums and their characteristics on the Dark Web. Exiting methods are mostly dependent on the continuous labeled contents, which are expensive and not feasible due to the nature of Dark Web data. Therefore, an approach that does not need a continuous availability of labeled forum and related knowledge is required. To this end, we propose an unsupervised model to identify and characterize Dark Web forums by combining clustering algorithm and decision tree algorithm. The proposed method presents the characteristics in an explainable form that can be used by the cyber threat intelligence system and law enforcement as scientific evidence to analyze any data breach or illicit activities in the Dark Web forums. To evaluate the performance of our model comprehensive experiments were conducted using real Dark Web forum data. The proposed approach achieves 98% accuracy and F1 score of 98% validating the efficacy of our proposed model to successfully characterize Dark Web forums. The experimental results suggest that the proposed model could be useful to the cyber threat intelligence and law enforcement community for building an intelligent source of knowledge that can be used for detecting data breach and illicit activities happening in the Dark Web forums.

Tau neutrinos at DUNE: New strategies, new opportunities

We propose a novel analysis strategy, that leverages the unique capabilities of the DUNE experiment, to study tau neutrinos. We integrate collider physics ideas, such as jet clustering algorithms in combination with machine learning techniques, into neutrino measurements. Through the construction of a set of observables and kinematic cuts, we obtain a superior discrimination of the signal ($S$) over the background ($B$). In a single year, using the nominal neutrino beam mode, DUNE may achieve $S/\sqrt{B}$ of $3.3$ and $2.3$ for the hadronic and leptonic decay channels of the tau respectively. Operating in the tau-optimized beam mode would increase $S/\sqrt{B}$ to $8.8$ and $11$ for each of these channels. We premier the use of the analysis software Rivet, a tool ubiquitously used by the LHC experiments, in neutrino physics. For wider accessibility, we provide our analysis code.

Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon

To examine the variability of travel behaviour over time, transportation researchers need to collect longitudinal data. The first studies around day-to-day variability of travel behaviour were based on surveys. Those studies have shown that there is considerable variation in individual travel behaviour. They have also discussed the implications of this variability in terms of modelling, policy evaluation or marketing. Recently, the multiplication of big data has led to an explosion in the number of studies about travel behaviour. This is because those new data sources collect lots of data, about lots of people over long periods. In the field of public transit, smart card data is one of those big data sources. They have been used by various authors to conduct longitudinal analyses of transit usage behaviour. However, researchers working with smart card data mostly rely on clustering techniques to measure variability, and they often use conceptual framework different from those of transportation researchers familiar with traditional data sources. In particular, there is no study based on smart card data that explicitly measure day-to-day intrapersonal variability of transit usage. Therefore, the purpose of this investigation is to address this gap. To do this, a clustering method and a similarity metric are combined to explore simultaneously interpersonal and intrapersonal variability of transit usage. The application is done with a rich dataset covering a 6 months period (181 days) and it contributes to the growing literature on smart card data. Results of this research confirm previous works based on survey data and show that there is no one size fits all approach to the problem of day-to-day variability of transit usage. They also prove that combining clustering algorithm with day-to-day intrapersonal similarity metric is a valuable tool to mine smart card data. The findings of this study can help in identifying new passenger segmentation and in tailoring information and services.

Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing factors including plate thickness and roll force in each rolling pass. For example, the estimation or prediction of the in-process thickness is utilized to select the control values (e.g., roll gap) in the next pass of rolling. However, adverse manufacturing conditions can interfere with accurate detection for such manufacturing factors. Although the state-of-the-art gamma-ray camera can be used for measuring the thickness, the outputs from it are influenced by adverse manufacturing conditions such as the high temperature of plates, followed by the evaporation of lubricant water. Thus, it is inevitable that there is noise in the thickness estimation. Furthermore, installing such thickness measurements for each passing step is costly. The precision of the thickness estimation, therefore, significantly affects the cost and quality of the final product. In this paper, we present machine learning (ML) technologies and models that can be used to predict the in-process thickness in the SPM operation, so that the measurement cost for the in-process thickness can be significantly reduced and high-quality steel plate production can be possible. To do so, we investigate most-known technologies in this application. In particular, Data Clustering based Machine Learning (DC-ML), combining clustering algorithms and supervised learning algorithms, is introduced. To evaluate DC-ML, two experiments are conducted and show that DC-ML is well suited to the prediction problems in the SPM operation. In addition, the source code of DC-ML is provided for the future study of machine learning researchers.

A Modified KNN Indoor WiFi Localization Method With K-median Cluster

Because of the serious attenuation and multi-path effect of GPS signal, outdoor-positioning technology can not be applied in complex indoor environment. Through the study of K-Nearest Neighbor applied in WiFi positioning, according to the problem that the time complexity of KNN algorithm increases linearly with the quantity of samples, this paper combined clustering algorithm with KNN optimized the similarity measure in fingerprint feature space and proposed a efficient indoor target location algorithm . Experimental results showed that the algorithm improved the positioning accuracy, had strong robustness to noise and more importantly, the positioning time was effectively shortened and it can meet the requirements of practical applications.

Fuzzy Clustering Algorithms in Combination with Other Segmentation Methods for Videos

Segmentation has crucial applications in computer vision, pattern recognition, and the processing of digital information. Video segmentation is a problem that crops up in lots of different contexts, such VOD, DVR, e-learning, GIS, and other similar systems. Pixels are roughly separated using a membership grading system that relies on a way of expanding the basic area. Motion capture and clustering of video segmentation is a major hurdle to overcome when obtaining and storing video data. Fuzzy c-means clustering is used here to divide up a movie into its constituent parts. Fuzzy theory provides a framework for describing scene changes based on fuzzy judgements. Because the data space is partitioned appropriately by the membership functions. The suggested method achieves a high degree of accuracy at a low rate of error.

Combining Clustering Algorithms for Provide Marketing Policy in Electronic Stores

Combining Clustering Algorithms for Provide Marketing Policy in Electronic Stores

Presenting a Model for Identifying the Best Location of Melli Bank ATMS by Combining Clustering Algorithms and Particle Optimization

Presenting a Model for Identifying the Best Location of Melli Bank ATMS by Combining Clustering Algorithms and Particle Optimization

A consistency-based validation for data clustering

Clustering analysis is a powerful tool in customer segmentation. Although various algorithms have been proposed, the determination of the optimal number of clusters remains to be a difficult issue. In this paper, a clustering method based on consistency criterion is proposed to address this issue. The main characteristic of the new approach is that it requires little prior information and can find the optimal number of clusters automatically. Extensive comparisons are done over 22 real-world datasets from different domains, in which four well-known clustering algorithms in combination with six clustering indices are used as the benchmark methods. The results demonstrate the superiority of our method in appropriately determining the number of clusters. An application of the new approach in customer segmentation of credit card users is also illustrated.

Frontier technologies of trust computing and network security

The increasing complexity of computer systems and communication networks induces tremendous requirements for trust and security. This special issue includes topics on trusted computing, risk and reputation management, network security and survivable computer systems/networks. These issues have evolved into an active and important area of research and development. The past decade has witnessed a proliferation of concurrency and computation systems for practice of highly trust, security and privacy, which has become a key subject in determining future research and development activities in many academic and industrial branches. This special issue aims to present and discuss advances of current research and development in all aspects of trusted computing and network security. In addition, this special issue provides snapshots of contemporary academia work in the field of network trusted computing. We prepared and organized this special issue to record state-of-the-art research, novel development and trends for future insight in this domain. In this special issue, 14 papers have been accepted for publication, which demonstrate novel and original work in this field. A detailed overview of the selected works is given below.