Traditional Cluster Analysis Research Articles

Scales and inferences

Abstract Scalar inferences (SIs) are upper-bounding inferences associated with the use of semantically lower-bounded scalar expressions. One of the current debates regarding these inferences concerns their inferential pattern, specifically whether SIs are uniform or diverse across scales. This study follows the work on scalar diversity yet introduces two changes: First, we reexamine, from a different perspective, two structural properties of scales identified as accounting for SI diversity (boundedness and distance). Second, we analyze our data using both traditional regression analysis and complementary cluster analysis. The regression analysis demonstrates that our reexamination of the structural properties provides a more effective model, which also emphasizes the relationship between boundedness and distance. Specifically, we propose that boundedness fixes distance. The cluster analysis demonstrates two scale types: given-scales, which have an entrenched scalar construal, trigger SIs robustly; and volatile-scales, which have a fluctuant scalar construal, trigger SIs inconsistently. Building on these two scale types, we propose a necessary distinction between the conceptualization of a scale, which is diverse across different scales, and the actual derivation of the SI, which is uniform for all scales, once a scale has been construed. This distinction, we propose, explains how diversity can coexist alongside uniformity.

MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications

Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuitionistic fuzzy c means clustering. IFCM considered an uncertainty parameter called hesitation degree and incorporated a new objective function which is based upon intutionistic fuzzy entropy in the conventional Fuzzy C-means. The clustering algorithm has membership and non membership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. Furthermore, the algorithm is extended for calculating membership and updating prototypes by minimizing the new objective function of weighted LINEX intuitionistic fuzzy c-means. Finally, the developed algorithms are illustrated through conducting experiments on random dataset, partition coefficient and validation function are used to evaluate the validity of clustering also this paper compares the results of proposed method with the results of existing basic intuitionistic fuzzy c-means.

Exploring the Robustness of Alternative Cluster Detection and the Threshold Distance Method for Crash Hot Spot Analysis: A Study on Vulnerable Road Users

Traditional hot spot and cluster analysis techniques based on the Euclidean distance may not be adequate for assessing high-risk locations related to crashes. This is because crashes occur on transportation networks where the spatial distance is network-based. Therefore, this research aims to conduct spatial analysis to identify clusters of high- and low-risk crash locations. Using vulnerable road users’ crash data of San Francisco, the first step in the workflow involves using Ripley’s K-and G-functions to detect the presence of clustering patterns and to identify their threshold distance. Next, the threshold distance is incorporated into the Getis-Ord Gi* method to identify local hot and cold spots. The analysis demonstrates that the network-constrained G-function can effectively define the appropriate threshold distances for spatial correlation analysis. This workflow can serve as an analytical template to aid planners in improving their threshold distance selection for hot spot analysis as it employs actual road-network distances to produce more accurate results, which is especially relevant when assessing discrete-data phenomena such as crashes.

Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries

In Industry 4.0, the production data obtained from the Internet of Things has reached the magnitude of big data with the emergence of advanced information and communication technologies. The massive and low-value density of big data challenges traditional clustering and correlation analysis. To solve this problem, a big data-driven correlation analysis based on clustering is proposed to improve energy and resource utilisation efficiency in this paper. In detail, the production units with abnormal and energy-intensive consumption can be classified by using clustering analysis. Additionally, feature extraction is carried out based on clustering analysis and the same cluster data is migrated to the training data set to improve correlation analysis accuracy. Then, correlation analysis can balance the relationship between energy supply and demand, which can reduce carbon emission and enhance sustainable competitiveness. The sensitivity analysis results show that the feature extraction method can improve the correlation analysis accuracy compared to the original analysis model. In conclusion, the big data-driven correlation analysis based on clustering can uncover the potential relationship between energy consumption and product yield, thus improving the efficiency of energy and resources.

Keeping track of cleantech development using innovation clusters and member’s website data: Evidence from leading energy clusters in Germany

The main research question addressed in this work is how energy clusters can be evaluated and what general conclusions can be drawn out of their activities. Traditional innovation cluster analysis approaches chiefly rely on surveys, interviews, open publications, and patents—lack of using updated activities of innovation clusters. Therefore, preceding cluster analysis methodologies always lack of providing up-to-date information. In this sense, analyzing energy cluster activities is an obvious interest for policymakers, investors, companies, etc. Moreover, such assessment help to track the development of new technologies, participation of different actors in an innovation ecosystem, and emerging topics in the energy sector. This work presents the research outcomes on the leading energy-innovation clusters in Germany. To this end, this paper exploits the publicly available website data from the clusters and member’s web-pages to investigate their geographical distribution, key focus areas, cluster, and member activities. In the course of the project, a web-scraping tool has been developed to crawl the clusters and member’s websites and scrape their text data. The tool performs systematic and guided web-scraping for searching a keyword presence on a particular web-page. In addition to this, data from commercially available company databases are used to complement the missing information from the website data. A total of 44 energy clusters along with 4524 members are taken into account in this study. The proposed methodology has shown that unstructured web-data is a valuable source for analyzing the clusters and their member’s innovation activities. Results have also indicated that there is a strong correlation (r=0.85) between Research and Development (R&D) expenditure and cluster count in individual federal states. The overall results have indicated that the majority of energy clusters are very specialized in certain topics, nevertheless, topics such as hydrogen, carbon, and bioenergy are getting notable attention from various stakeholders. Simultaneously, various cross-sectoral topics are also emerging due to the coupling between different sectors. Findings could help policymakers and federal innovation agencies to understand the ongoing progress in cleantech innovation activities. From the methodological point of the view, it provides an underlying ground to access the impact of cluster policies.

Gaia colour-magnitude diagrams of young open clusters

Context. Automated analyses of Gaia astrometric data have led to the discovery of many new high-quality open cluster candidates. When a good determination of their parameters is available, these objects become excellent tools for investigating the properties of our Galaxy. Aims. We explore whether young open clusters can be readily identified from Gaia data alone by studying the properties of their Gaia colour-magnitude diagrams. We also want to compare the results of a traditional cluster analysis with those of automated methods. Methods. We selected three young open cluster candidates from the UBC catalogue, ranging from a well-populated object with a well-defined sequence to a poorly populated and poorly defined candidate. We obtained classification spectra for the brightest stars in each. We redetermined members based on EDR3 data and fitted isochrones to derive the age, distance, and reddening. Results. All three candidates are real clusters with ages below 100 Ma. UBC 103 is a moderately populous cluster, with an age around 70 Ma. At a distance of ∼3 kpc, it forms a binary cluster with nearby NGC 6683. UBC 114 is a relatively proximal (∼1.5 kpc) poorly populated cluster containing two early-B stars. UBC 587 is a dispersed and very young (≤10 Ma) cluster located at ∼3 kpc, behind the Cygnus X region, which may be a valuable tracer of the Orion arm. Conclusions. The OCfinder methodology for the identification of new open clusters is extremely effective, with even poor candidates resulting in interesting detections. The presence of an almost vertical photometric sequence in the Gaia colour-magnitude diagram is a safe way to identify young open clusters. Automated methods for the determination of cluster properties give approximate solutions, but these are still subject to difficulties. There is some evidence suggesting that artificial intelligence systems may systematically underestimate extinction, which may impact in the age determination.

Variable Selection for Meaningful Clustering of Multitopic Territorial Data

This paper proposes a new methodology to improve territorial cohesion in clustering processes where many variables from different topics are considered. Clustering techniques provide added value to identify typologies, but there are still unsolved challenges when data contain an unbalanced number of variables from different topics. The territorial feature selection method (TFSM) is presented as a method to select the representative variable of each topic such that the interpretability of resulting clusters is preserved and the geographical cohesion is improved with respect to classical approaches. This paper also introduces the thermometer as a new knowledge acquisition tool that allows experts to transfer semantics to the data mining process. TFSM proposes the index of potential explainability (Ek) as the criteria to select the most promising variables for clustering. Ek is based on the combination of inferential testing and metrics such as support. The proposal is applied with the INSESS-COVID19 database, where territorial groups of vulnerable populations were found. A set of 195 variables with 21 unbalanced thematic blocks is used to compare the results with a traditional multiview clustering analysis with promising results from both the geographical and the thematic point of view and the capacity to support further decision making.

A new adaptive membership function with CUB uncertainty with application to cluster analysis of Likert-type data

Likert-type scales are commonly used in both academia and industry to capture human feelings since they are user-friendly, easy-to-develop and easy-to administer. This kind of scales generate ordinal variables made up of a set of rank ordered items. Since the distance between two consecutive items cannot be either defined or presumed equal, this kind of variable cannot be analysed by either statistical methods defined on a metric space or parametric tests. Therefore, Likert-type variables cannot be used as segmentation variables of a traditional cluster analysis unless pre-transformed. In such context, fuzzy numbers have been suggested as a way to recode Likert-type variables. Fuzzy numbers are defined by a membership function whose form is usually determined by an expert. In practice, researchers usually define one membership function for each Likert-type scale, not considering the peculiar characteristics of neither questions nor respondents. In this way, the individual uncertainty against each question is considered equal and constant. To overcome this limitation and to reduce the expert’s subjectivity, in this study an adaptive membership function based on CUB model is suggested to pre-transform Likert-type variables into fuzzy numbers before the adoption of a clustering algorithm. After a theoretical presentation of the method, an application using real data will be presented to demonstrate how the method works.

Manifold classification of neuron types from microscopic images.

Freely available at https://github.com/Mr-strlen/Cell_Pattern_Analysis_Tool.'

Analysis of the Intelligent Tourism Route Planning Scheme Based on the Cluster Analysis Algorithm.

In view of the problems of the traditional cluster analysis algorithm such as strong dependence on the initial cluster center, the traditional k-means cluster analysis algorithm is improved and the experiment proves that the improved algorithm has a better clustering effect; in view of the problems of the traditional tourism route planning, the improved k-means cluster analysis algorithm is applied to the intelligent tourism route planning scheme design and an intelligent tourism planning scheme based on the cluster analysis algorithm is proposed; the tourists' preference metric is fully considered, and the experimental results show that the scheme has certain reasonableness and reference value.

Fuzzy Logic: adding natural uncertainties into environmental assessment

This research study sought to evaluate aimed at evaluating the possible advantages of using Fuzzy Logic as opposed to Boolean Logic to assess environmental contamination and source appraisal for polycyclic aromatic hydrocarbons (PAH). Results obtained through traditional assessment tools for two different tropical coastal areas through using traditional clustering and principal components analysis were compared with those derived from the Fuzzy Logic, using the by Fuzzy C-means algorithm. The feedings achieved through Fuzzy Logic showed a greater qualitative detail than those derived from traditional tools. The abrupt and unnatural changes obtained from the usual classification methods were avoided by having membership values varying continuously in space, providing a more accurate picture of environmental contamination in complex and multiple sources environments. Furthermore, by not depending on statistic suppositions distribution of data like other methods, becomes more suitable for environmental data. Although Fuzzy Logic does not produce quantitative interpretations, its application generates adequate the data needed to avoid environmental management bias in the inference of contamination sources.

Consumer profiles of women’s football spectators

ABSTRACT The purpose of this study was the segmentation of women’s football spectators based on their hierarchical involvement. Furthermore, distinct consumer profiles were derived by enriching the segments with socio-economic, behavioural, and psychological variables. Using a survey, data were collected during a German women’s national football match (n = 693). The psychological continuum model was employed to segment the spectators. Four distinct consumer profiles, hierarchically aligned based on involvement, were created. The results indicated an increase in the consumer’s psychological connectedness while showing more robust behavioural patterns dependent on the involvement stages. Practitioners should acknowledge the four different stages of involvement, which align with different ages and income levels. This research contributed to the existing literature by supplementing the scant research using the psychological continuum model (PCM) as a segmentation tool over traditional cluster analysis on passive sport consumption.

Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems

This paper uses a radial basis function (RBF) transformation of data envelopment analysis (DEA) data to perform RBF-DEA. It is shown that the RBF-DEA frontier identifies cases that have average efficiency scores in traditional DEA. The formal identification of average efficiency cases allows decision-makers to use these cases and related information for regression, segmentation and cluster analysis. Additionally, negative inputs and outputs can be used in RBF-DEA and unique ranking of fully efficient cases in traditional DEA can be achieved by further evaluating these fully efficient cases against the average RBF-DEA regression frontier. When compared to traditional cluster analysis, RBF-DEA cluster analysis offers unique advantages in that number of clusters do not need to be mentioned and cluster labels are identified by the RBF-DEA technique. Furthermore, unlike the traditional techniques, RBF-DEA cluster memberships are not sensitive to any initial random starting points.

Logistics service provider selection decision making for healthcare industry based on a novel weighted density-based hierarchical clustering

In recent years, Industry 4.0 makes a significant impact on the manufacturing industry, which enables the business more intelligent and efficient, all while minimizing costs. As known, the logistics concerns in the supply chain always play an important role to a manufacturing company, and decision on the selection of logistics service provider is a key point, especially for healthcare manufacture whose products are the medical devices or equipment of fragility and high cost. Practically there are so many logistics service providers with varieties in service quality, effectiveness, punctuality and reliability, that the manufacturers often encounter the challenge on the provider selection, and healthcare industry is no exception. However, the research on provider selection for healthcare manufacturers is quite limited. In order to help them to make the decision, this paper designs a logistics service provider selection scheme based on a novel weighted density-based hierarchical cluster analysis with integration of the analytic hierarchy process (AHP) for healthcare industry. Initially an evaluation index system reflecting the capability of the candidate providers in all aspects is established. To improve the clustering within the scheme, the density concept and the obtained weights are introduced into the traditional hierarchical cluster analysis (HCA) to shape a novel Weighted Density-Based HCA (WDBHCA). To validate the feasibility of the scheme, a case study on a specified healthcare industry manufacturer is carried out, and results fulfill the case company’s requirement which shows the feasibility of the proposed provider selection scheme. In addition, this scheme can be applied to the provider selection in other fields, as well.

GAEBic: A Novel Biclustering Analysis Method for miRNA-Targeted Gene Data Based on Graph Autoencoder

Unlike traditional clustering analysis, the biclustering algorithm works simultaneously on two dimensions of samples (row) and variables (column). In recent years, biclustering methods have been developed rapidly and widely applied in biological data analysis, text clustering, recommendation system and other fields. The traditional clustering algorithms cannot be well adapted to process high-dimensional data and/or large-scale data. At present, most of the biclustering algorithms are designed for the differentially expressed big biological data. However, there is little discussion on binary data clustering mining such as miRNA-targeted gene data. Here, we propose a novel biclustering method for miRNA-targeted gene data based on graph autoencoder named as GAEBic. GAEBic applies graph autoencoder to capture the similarity of sample sets or variable sets, and takes a new irregular clustering strategy to mine biclusters with excellent generalization. Based on the miRNA-targeted gene data of soybean, we benchmark several different types of the biclustering algorithm, and find that GAEBic performs better than Bimax, Bibit and the Spectral Biclustering algorithm in terms of target gene enrichment. This biclustering method achieves comparable performance on the high throughput miRNA data of soybean and it can also be used for other species.

IKM-NCS: A Novel Clustering Scheme Based on Improved K-Means Algorithm

Aiming at the problems of distorted center selection and slow iteration convergence in traditional clustering analysis algorithm, a novel clustering scheme based on improved k-means algorithm is proposed. In this paper, based on the analysis of all user behavior sets contained in the initial sample, a weight calculation method for abnormal behaviors and an eigenvalue extraction method for abnormal behavior set are proposed and a set of abnormal behaviors is constructed for each user according to the behavior data generated by abnormal users. Then, on the basis of the traditional k-means clustering algorithm, an improved algorithm is proposed. By calculating the compactness of all data points and selecting the initial cluster center among the data points with high and low compactness, the clustering performance is enhanced. Finally, the eigenvalues of the abnormal behavior set are used as the input of the algorithm to output the clustering results of the abnormal behavior. Experimental results show that the clustering performance of this algorithm is better than the traditional clustering algorithm, and can effectively improve the clustering performance of abnormal behavior

Analysis of Transportation Companies in the Czech Republic by the Kohonen Networks - Identification of Industry Leaders

Computational models of artificial neural networks are currently used in different areas. Accuracy of results exceeds the performance of traditional statistical techniques. Artificial neural networks as the Kohonen map may be used e.g. to identify industry leaders, thus replacing the traditional cluster analysis and other methods. The aim of this contribution is to analyse the transportation industry in the Czech Republic by the Kohonen networks and identify industry leaders. The data file contains results - division of companies into a total of 100 clusters. Each cluster is subjected to analysis of absolute indicators and several parameters, average, as well as absolute, are examined. In total, 88 firms may be considered as industry leaders. Consequently, a fairly small group of companies has a strong influence on development of the whole transportation industry in the Czech Republic.

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

BackgroundIn the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging.ObjectiveThis study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies.MethodsWe searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process.ResultsOur literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification.ConclusionsThis review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.

Cluster Analysis of Haze Episodes Based on Topological Features

Severe haze episodes have periodically occurred in Southeast Asia, specifically taunting Malaysia with adverse effects. A technique called cluster analysis was used to analyze these occurrences. Traditional cluster analysis, in particular, hierarchical agglomerative cluster analysis (HACA), was applied directly to data sets. The data sets may contain hidden patterns that can be explored. In this paper, this underlying information was captured via persistent homology, a topological data analysis (TDA) tool, which extracts topological features including components, holes, and cavities in the data sets. In particular, an improved version of HACA was proposed by combining HACA and persistent homology. Additionally, a comparative study between traditional HACA and improved HACA was done using particulate matter data, which was the major pollutant found during haze episodes by the Klang, Petaling Jaya, and Shah Alam air quality monitoring stations. The effectiveness of these two clustering approaches was evaluated based on their ability to cluster the months according to the haze condition. The results showed that clustering based on topological features via the improved HACA approach was able to correctly group the months with severe haze compared to clustering them without such features, and these results were consistent for all three locations.

Functional clustering analysis of Chinese provincial wind power generation

China is a broad territory country. There are significant differences in the terrain, climate, and other environmental factors between different provinces, which affect wind power generation. In order to better analyze the situation of wind power generation in Chinese provinces, this paper uses the functional clustering analysis to classify the monthly data of wind power generation in 30 Chinese provinces from March 2013 to October 2019. The empirical results of this paper show that the wind energy generation in Chinese provinces can be divided into three categories, and the results are consistent with the actual situation. In this paper, functional clustering analysis is used to analyze monthly data, compared with the traditional clustering analysis to analyze annual data which are obtained by accumulated monthly data. Higher-dimensional data can be used for analysis to reduce information loss. Moreover, data can be viewed as functions, and more information can be mined by analyzing derivative functions, and so on. The analysis of wind energy generation has certain guiding significance for the development and utilization of renewable energy.