Application of big data technology and clustering algorithm in business activities

With the rapid development of big data technology, its application in the field of new media is becoming more and more extensive, especially in personalized service shows great potential. This paper discusses in depth the application, challenges and future development of big data technology in new media personalized service. The following conclusions are drawn: (1) Big data technology plays a key role in new media personalized service Big data technology can construct a refined user profile, realize personalized content recommendation, and improve user satisfaction and loyalty by collecting, analyzing and utilizing user behavioral data on new media platforms. (2) Big data technology faces multiple challenges in new media personalized services. Although big data technology shows great potential in new media personalized service, it still faces challenges in data privacy and security, data quality and accuracy, technology update and algorithm optimization, as well as regulatory compliance and ethics. (3) Innovative applications promote the development of big data technology in new media personalized services. With the continuous innovation and application of big data technology, new media personalized services are also developing and improving.

After the outbreak of COVID-19, due to the limitations of traditional epidemic prevention methods, the Chinese government actively applied big data technology to pandemic prevention and control. This article takes the whole process perspective to summarize the application of big data technology for pandemic prevention and control in the emergency prevention stage and normalized prevention stage separately. The previous studies have explored the application of big data technology mainly focused on the early monitoring and early warning of epidemic but have not yet shed light on the perspective of full process. Second, this article identifies the main shortcomings of big data technology application in pandemic prevention and control and stresses the healthy development of big data technology is inseparable from effective supervision. The application and value of big data technology have brought new enlightenment to China’s emergency management, including establishing a new big data service platform, improving “social listening”, and building resilient smart cities with a digital emergency management system.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.

With the rapid development of the Internet, big data technology has received widespread attention in the field of management. Traditional project schedule management methods cannot adapt to the complex and changing environment, and some researchers believe that big data technology has become a key tool to improve and optimize project management. However, there is a lack of mature theoretical systems and practical experience to guide the application of big data technology. Based on the case of Amazon, this paper analyses how the company uses big data technology to optimize project schedule management. It is found that the application of big data technology in project management brings significant benefits. It is able to provide empirical and innovative support for project schedule management. This paper discusses the application of big data technology in project schedule management, highlighting its capability to enable timely adjustments. Real-time monitoring and forecasting facilitate proactive management of project directions and potential risks. Moreover, it leverages real-time data to optimize resource allocation and enhance resource utilization. Furthermore, the paper addresses the challenges and proposes countermeasures associated with big data technology in this context. To tackle the issue of uneven data quality, a robust data collection mechanism should be established.

PurposeThe K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.Design/methodology/approachThis study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.FindingsThe performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.

With the continuous improvement of agricultural mechanization level, the role of supply chain management in agricultural production is becoming increasingly significant. The article aims to explore the application of big data technology in agricultural mechanization supply chain and the challenges it faces. Through remote sensing technology, Internet of Things technology, data mining, and machine learning methods, remote monitoring and intelligent management of agricultural machinery and equipment can be achieved, as well as information management of agricultural product production and circulation links, further optimizing resource allocation and enhancing the quality and market competitiveness of agricultural products. Research has found that big data technology can significantly improve the overall efficiency and response speed of agricultural mechanization supply chains, supporting agricultural producers to make more scientific and reasonable decisions. However, challenges such as data security, data quality, and data privacy also arise. To address these challenges, the article proposes implementing multi-layer encryption and access control, adopting advanced data cleaning techniques and verification mechanisms, and implementing strict data classification and anonymization processing strategies.

Clustering plays a fundamental task in the process of data mining, which remains more demanding due to the ever-increasing dimension of accessible datasets. Big data is considered more populous as it has the ability to handle various sources and formats of data under numerous highly developed technologies. This paper devises a robust and effective optimization-based Internet of Things (IoT) routing technique, named Student Psychology Based Optimization (SPBO) -based routing for the big data clustering. When the routing phase is done, big data clustering is carried out using the Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) approach. Here, the Mapreduce framework is used to minimizing the over fitting issues during big data clustering. The process of feature selection is performed in the mapper phase in order to select the major features using Minkowski distance, whereas the clustering procedure is carried out in the reducer phase by Deep FC-IIWO fuzzy clustering, where the FC-IIWO technique is designed by the hybridization of Improved Invasive Weed Optimizer (IIWO) and Fractional Calculus (FC). The developed SPBO-based routing approach achieved effective performance in terms of energy, clustering accuracy, jaccard coefficient, rand coefficient, computational time and space complexity of 0.605 J, 0.935, 0.947, 0.954, 2100.6 s and 72KB respectively.

The emergence of big data technology continues to innovate and change the world, bringing opportunities and challenges to all walks of life. Against the background of this era, traditional entrepreneurship education requires reform and innovation. This research attempts to explore the ways and practices of applying big data technology to entrepreneurship education so as to improve and perfect traditional entrepreneurship education and achieve its sustainable development. Based on classic theories, such as entrepreneurial theory, strategic management theory, and leadership theory, this paper develops a relatively systematic attribute system of entrepreneurship education under big data technology, comprehensively uses Fuzzy-DEMATEL and ISM methods to explore the relationship between different attributes and their importance, and finally constructs a hierarchical framework for the application of big data technology in entrepreneurship education. The results show that the attributes of entrepreneurship education under big data technology can be divided into four levels, each with different priorities and degrees of importance, and there are complex interactions and constraints among them. This study provides important guidance and suggestions for the development of entrepreneurship education and multiattribute decision-making management under the given resources, which is conducive to the sustainable development of entrepreneurs and new ventures.

One of the important features of the “new information age” is “big data”. In the age of big data, with the introduction of student information management systems, colleges and universities are increasingly paying attention to systematically managing student information management and improving their efficiency, effective management of student information and innovative development to build a new model of management system. In studying the evolution of student information management, big data technologies emerging in the information age play an important role. By leveraging big data to improve systematic management of student information, colleges and universities can manage student information faster and more efficiently and make new contributions to the advancement of student information management. In this article, information management is considered as the theoretical basis for research, big data technology is used as the main search algorithm, and its important content is combined to analyze and study the possibilities of the student information management system. In this article, an information management system for university students is used as a research object, which is optimized and improved through big data technologies. Big Data technology can be seen as a kind of algorithm for analyzing and processing data. Therefore, it can be used to create a student information management strategy. The experimental results show that this research has a better impact on the creation and implementation of an information management system for students using Big Data technology and the application of Big Data technology in the information management system for students.

The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.

In the traditional marketing mode of fast-selling products, the sales mode of physical stores is adopted. However, in the background of the current big data era, it is a trend to optimize the promotion form by applying big data technology. Therefore, this paper puts forward the application research of big data in the promotion of fast-selling products. This paper makes an indepth study on big data technology and commodity marketing. It is believed that there is a lot of information hidden behind the information data, and the application of big data technology pays more attention to consumer behavior than before. In this paper, according to the characteristics of fast-selling products promotion activities, combined with big data technology, the effect evaluation model are established, which can better solve the shortcomings of traditional promotion activities which are difficult to evaluate. And according to the actual needs of the promotion of fast-selling products, targeted optimization is carried out. In order to further verify the data analysis ability of big data technology in the promotion of fast selling products, this paper establishes the corresponding investigation experiment. The experimental data show that big data technology can better analyze the actual effect of various promotion tools and promotion strategies, provide technical support for enterprises before and after the promotion of fast-selling products, and facilitate enterprises to adjust strategies and summarize experience. The analysis shows that big data technology has brought a variety of convenience to the promotion activities, which not only broadens the sales channels, but also provides a new basis for the decision-making of enterprises.

Big data has brought too much convenience to our work and life. Through the development of big data, people are full of curiosity about all new things, especially more and more people are obsessed with Ming and Qing literature. The main purpose of this article is to study the application of big data technology in Ming and Qing literature. In this paper, under the social background of the era of big data, further study how Ming and Qing literature developed and the relationship between the two, and then continue to promote social development and progress. The experimental results of this article show that the user scale of Ming and Qing literature under big data technology has increased by 19%, and the application of big data technology in Ming and Qing literature has become more and more extensive, and its positioning in the cultural market has also been significantly improved. This is also conducive to creating more valuable literary works.

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.

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.