Data Clustering Algorithm Research Articles

Study on the Role of Big Data Technology in Promoting the Transformation of Financial Accounting in the Digital Economy Era

Abstract This paper examines big data technology through the use of big data clustering algorithms and examines the factors that influence the transformation of financial accounting in the digital economy. Separate the clustering center to rectify it, finish accurate clustering of the original data set, and normalize the data. The data center point results are output after dividing the data set, and the final clustering results of the data set are obtained according to the similarity coefficient through the continuous iterative execution process. The results show that the comprehensive scores after the transformation of financial accounting have all risen sharply, and the score of the work content indicator in financial risk avoidance has risen from 4 points before the transformation to 9.5 points. It shows that big data technology can effectively promote the transformation of financial accounting in the digital economy era.

Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model.

Prediction and Distribution of Disease Using Hybrid Clustering Algorithm in Big Data

COVID disease plague of 2019 (COVID19) has made an overall health related crisis with a very high gamble of spreading and influencing the whole planet. In essentially every nation, new cases have been accounted. To identify all countries expanding number of tests, the manual clustering of COVID-19 and clinical infection information tests becomes tedious and requires profoundly talented work. As of late, a few calculations have been utilized for clustering clinical datasets deterministically; nonetheless, these definitions have not been powerful in gathering and investigating clinical infections. To rank and score more than 200 nations as indicated by COVID-19 cases and casualty in 2020 and contrast the outcomes with existing pandemic weakness forecast models and results produced by standard Data clustering scoring methods. Information clustering is a course of orchestrating comparative information into gatherings. A clustering algorithm bundles an informational collection into a few several clusters such an extent that the similitude inside a gathering is better compared to among clusters. This paper propose new Hybrid clustering algorithm KMHC in view of K-Means and Hierarchical Clustering calculation. This calculation KMHC, First isolated into every nation gatherings or fragments in light of the COVID patients count, Secondly grouping models has been made across the nations on the planet and across the states in India, and the presentation investigation is analyzed. This paper likewise center figures the future COVID count for India. By utilizing this outcome, set of nations which are having higher COVID count can be effortlessly pictured and the proper moves will be made to diminish the count.

Exploring new subgroups for irritable bowel syndrome using a machine learning algorithm

Irritable bowel syndrome (IBS) is a complicated gut-brain axis disorder that has typically been classified into subgroups based on the major abnormal stool consistency and frequency. The presence of components other than lower gastrointestinal (GI) symptoms, such as psychological burden, has also been observed in IBS manifestations. The purpose of this research is to redefine IBS subgroups based on upper GI symptoms and psychological factors in addition to lower GI symptoms using an unsupervised machine learning algorithm. The clustering of 988 individuals who met the Rome III criteria for diagnosis of IBS was performed using a mixed-type data clustering algorithm. Nine sub-groups emerged from the proposed clustering: (I) High diarrhea, pain, and psychological burden, (II) High upper GI, moderate lower GI, and psychological burden, (III) High psychological burden and moderate overall GI, (IV) High constipation, moderate upper GI, and high psychological burden, (V) moderate constipation and low psychological burden, (VI) High diarrhea and moderate psychological burden, (VII) moderate diarrhea and low psychological burden, (VIII) Low overall GI, and psychological burden, (IX) Moderate lower GI, and low psychological burden. The proposed procedure led to the discovery of new homogeneous clusters in addition to certain well-known Rome sub-types for IBS.

A 3D organized point cloud clustering algorithm for seismic fault data based on region growth

A 3D organized point cloud clustering algorithm for seismic fault data based on region growth

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

Abstract In this paper, a dataset is obtained by clustering data through a big data clustering algorithm, and a multidimensional state space vector with chaotic trajectories is established by reconstructing the phase space after several iterations to calculate the maximum and minimum similarity distances. The clustering between the localized imagery and narratives of contemporary oil paintings is realized based on the extracted chaotic associative dimensional features. A total of 80 students from an art class at a school were selected to conduct an imagery analysis of the cultural roots of imaginative oil painting narratives, in which the evaluation score of the research class in the imagery of shape and color could reach 10 points. Therefore, the localization of oil painting is a theoretical concept for a Chinese oil painting to go global, which meets the realistic needs and development trend of Chinese oil painting.

Medium-term trends in economic and technological development of metals industry regions

Since metals industry regions are dependent on the location of production resources and infrastructure, as well as have highly capital-intensive production, they usually feature a slowly changing industrial and technological structure of production. In line with the hypothesis of the study, the technological structure of the manufacturing industry determines the differences in their economic development. The study aims to explore the medium-term trends and patterns in economic and technological development of the metals industry regions. The methodological basis of the research is the structural dynamic approach to studying regional economic development. The methods include multivariate data analysis and clustering algorithms, time series estimation and correlation analysis. The study uses the data on large and medium-sized enterprises by types of economic activities for 2006–2021 taken from an information and analytics system FIRA PRO. According to the findings, the Chelyabinsk, Sverdlovsk, Lipetsk, Vologda and Tula oblasts are metals industry regions. The identified medium-term trends in their economic development show different dynamics of input and output indicators of production both by all types of economic activities and in metals industry in particular. The study proves that the dynamics of metals industry regions’ development largely depends on technological characteristics of the manufacturing industry. The results of the research contribute to the understanding of the adaptation mechanisms of regions’ industry to the changing conditions of external environment.

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolfs and three worst omega wolfs of the population are used. So, the best wolfs and worst omega wolfs assist in moving the new solutions towards the best solutions and simultaneously helps in staying away from the worst solutions. This enhances the chances of reaching the near optimal solutions. The superiority of the proposed method is compared with five promising algorithms, namely GWO, Sine-Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), JAYA and K-means algorithms. The result obtained from the Duncan’s multiple range test and Nemenyi hypothesis based statistical test confirms the superiority and robustness of our proposed method.

Three Fuzzy c-Shapes Clustering Algorithms for Series Data

Various fuzzy clustering algorithms have been proposed for vectorial data. However, most of these methods have not been applied to series data. This study presents three fuzzy clustering algorithms for series data based on shape-based distances. The first algorithm involves Shannon entropy regularization of the k-shape objective function. The second algorithm is similar to the revised Bezdek-type fuzzy c-means algorithm obtained by replacing the membership of the hard c-means objective function with its power. The third algorithm involves Tsallis entropy regularization of the objective function of the second algorithm. Theoretical observations revealed that the third algorithm is a generalization of the first and second algorithms, which was validated by numerical experiments. Furthermore, numerical experiments were performed using 11 benchmark datasets to demonstrate that the third algorithm outperforms the others in terms of accuracy.

Research on Hybrid Data Clustering Algorithm for Wireless Communication Intelligent Bracelets

Wireless communication smart bracelet data include motion data, sleep time data, heart rate and blood pressure data and positioning data, etc. These data have diversity and high complexity, and there are interconnections or interactions between the data, which have high clustering difficulty. To this end, a new data clustering algorithm is studied for wireless communication smart bracelets. The K-medoids algorithm is used to calculate the intra-cluster, inter-cluster, or overall similarity to complete the initial clustering of the bracelet data. Setting the clustering evaluation index can determine the optimal number of clusters. The data objects that are closely surrounded and relatively dispersed are selected as the initial clustering centers and combined with the new index IXB to complete the improvement of the data clustering algorithm. The test results show that the accuracy, recall, and F1 of the research algorithm for clustering the heart rate monitoring dataset, temperature monitoring dataset, energy consumption dataset, and sleep monitoring dataset are higher than 97%, which indicates that the data clustering effect of the algorithm is good.

Retracted: Library Management System Based on Data Mining and Clustering Algorithm

Retracted: Library Management System Based on Data Mining and Clustering Algorithm

GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry.

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker's intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.

Retracted: Construction of Student Information Management System Based on Data Mining and Clustering Algorithm

Retracted: Construction of Student Information Management System Based on Data Mining and Clustering Algorithm

HSGS: A hybrid of harmony search algorithm and golden section for data clustering

HSGS: A hybrid of harmony search algorithm and golden section for data clustering

A Novel Possibilistic Clustering Algorithm for Measurement Data of Vehicle MMW Radar

A Novel Possibilistic Clustering Algorithm for Measurement Data of Vehicle MMW Radar

MRI Brain Images Mapping for Tumour Detection Using CNN

Brain tumor is a serious life-threatening disease which occurs due to peculiar growth of cells or tissues present in brain. In recent times it is becoming a considerable cause of death of many people. The seriousness of this tumor growing in brain is very huge when compared to all other varieties of cancers and tumors. Hence, to save the affected people detection of the tumor and proper treatment should be done instantaneously without any delay. In this new age of technology, Machine Learning (ML) and Deep Learning (DL) models can be utilized to identify the tumor at early stages more precisely so that proper medication can be given to the affected person which will help in curing them. This paper proposes two different machine learning models to identify the brain tumor by analysing the Magnetic Resonance Image (MRI) scans of the brain. Both unsupervised and supervised learning models were implemented to detect the tumors in brain. Fuzzy C means is used as a part of unsupervised learning model, it is a data clustering algorithm in which entire data set is grouped into predefined number of clusters with every data point belonging to every cluster to a specific degree of membership value. In this approach tumor region is treated as one cluster and healthy brain is another cluster. Moving forward, as a part of supervised learning, transfer learning approach is implemented for classifying whether the given input MRI scan consists of tumor or not. Visual Geometric Group (VGG-19) model was used which is a 19-layer deep pre-trained neural network architecture for better accuracy and results. All the models were developed using python in jupyter notebook.

Retracted: Evaluation of English Proficiency Based on Big Data Clustering Algorithm

Retracted: Evaluation of English Proficiency Based on Big Data Clustering Algorithm

Efficient fracture network characterization in enhanced geothermal reservoirs by the integration of microseismic and borehole logs data

Characterization of 3D fracture networks induced by hydraulic fracturing in enhanced geothermal reservoirs is crucial for safe and effective energy recovery from deep geothermal reservoirs. Microseismic monitoring is one of the most promising ways to infer the geometry of fracture network. However, the identification of fracture network from microseismic events is still a challenging task, attributed to high noises in microseismicity interpretation and fuzzy relationships between microseismic event locations and fracture planes. This study proposed a novel methodology to identify fracture network from the spatial distribution of microseismic events. The method was composed by (1) density-based spatial clustering algorithm for microseismic data denoising, (2) Monte Carlo optimization algorithm for fractures localization by minimizing the total distance between denoised microseismic events and fracture planes, and (3) Elbow method to decide the most likely number of fractures. Moreover, the method employed the prior knowledge of geo-stresses and initial fracture patterns inferred from borehole logs to constrain the geometry of fracture network. Following verifications with synthetic fractures and microseismic events, the method was implemented in a realistic enhanced geothermal system, which reliably identified the geometry of 3D fracture network induced by hydraulic fracturing.

Two Medoid-Based Algorithms for Clustering Sets

This paper proposes two algorithms for clustering data, which are variable-sized sets of elementary items. An example of such data occurs in the analysis of a medical diagnosis, where the goal is to detect human subjects who share common diseases to possibly predict future illnesses from previous medical history. The first proposed algorithm is based on K-medoids and the second algorithm extends the random swap algorithm, which has proven to be capable of efficient and careful clustering; both algorithms depend on a distance function among data objects (sets), which can use application-sensitive weights or priorities. The proposed distance function makes it possible to exploit several seeding methods that can improve clustering accuracy. A key factor in the two algorithms is their parallel implementation in Java, based on functional programming using streams and lambda expressions. The use of parallelism smooths out the O(N2) computational cost behind K-medoids and clustering indexes such as the Silhouette index and allows for the handling of non-trivial datasets. This paper applies the algorithms to several benchmark case studies of sets and demonstrates how accurate and time-efficient clustering solutions can be achieved.

Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region

Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region