Clustering Algorithm For Big Data Research Articles

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types.To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management.

Big data clustering algorithm of power system user load characteristics based on K-means and SOM neural network

Big data clustering algorithm of power system user load characteristics based on K-means and SOM neural network

Big data clustering based on spark chaotic improved particle swarm optimization

In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.

Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm

Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study constructs a customer segmentation marketing strategy model that integrates support vector machines and clustering algorithms. This model first utilizes support vector machines to segment existing customer data, and then integrates support vector machines and clustering algorithms to construct a customer segmentation model. Finally, simulation experiments are conducted using the dataset. The results show that the model algorithm obtains the optimal solution when the quantity of iterations is 50. Meanwhile, the average error rate of the model algorithm in the customer segmentation process is 6.82%, the average recall rate is 91.28%, and the average profit predicted by the impact strategy developed by the segmentation model is 29.88%, which is 2.53% different from the true value.

Retracted: Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

Retracted: Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

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.

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.

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: Evaluation of English Proficiency Based on Big Data Clustering Algorithm

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

Retracted: Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

Retracted: Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

Retracted: Multidimensional Discrete Big Data Clustering Algorithm Based on Dynamic Grid

Retracted: Multidimensional Discrete Big Data Clustering Algorithm Based on Dynamic Grid

An Analysis of Clustering Algorithms for Big Data

An Analysis of Clustering Algorithms for Big Data

Data-Oriented Intelligent Judgment Platform for Red Tourism Industry in Northeast Guangdong Based on Tourism Big Data Clustering Algorithm

Defining big data in the context of tourism forecasting, summarizes the changes it brings to tourism business decision-making. The experimental part deals with the tourism metadata shared by the website, and applies a suitable clustering algorithm to generate the density pattern of the most frequently visited places by tourists. At the same time, it proposes a method to obtain local core categories through the discovery of maximal cliques, and proposes a parallelization of the maximal clique discovery algorithm. strategy, and then propose a parallel strategy for the entire algorithm and experiment on real datasets. Realize the intelligence, intelligence, and semantics of the video surveillance network, so that the police can be freed from the labor of watching video surveillance. Through the extraction and correlation of data feature attributes, the collision can obtain "object laws", so as to make efficient and accurate decisions.

ABOUT ONE APPROACH TO THE CONSTRUCTION OF SELF-ADAPTIVE ALGORITHMS BASED ON DISTRIBUTION MIXTURES

This article presents a novel approach for constructing self-optimizing algorithms designed to estimate parameters (hyperparameters) in complex systems, with a broader application to classical genetic and evolutionary algorithms. The central theme of this paper revolves around the exploration of multimodality in the objective function and advocates the effectiveness of employing distribution mixtures as opposed to single-peaked distributions in traditional scenarios. A significant focus of this research involves addressing the challenge of determining the dimensionality of the mixture and developing algorithms for both augmenting and reducing it. The methods employed for manipulating the mixture’s dimensionality are inspired by cluster analysis techniques, specifically those utilized in the CURE and BIRCH big data clustering algorithms. Furthermore, this work delves into a detailed examination of a self-adaptive algorithm grounded in a mixture of distributions, illustrated by the CMA-ES algorithm. It is evident that the proposed approach outlined in this paper exhibits versatility, making it applicable not only to the CMA-ES algorithm but also to various optimization algorithms involved in tasks such as classification or regression recovery.

Application of Big Data Clustering Algorithm in Electrical Engineering Automation

The existing control methods have the problem of imperfect automatic distribution linkage model, which leads to excessive noise in the process of practical application. This paper designs an electrical engineering automation control method based on big data clustering algorithm, obtains the load parameters of power cable laying mode, arranges the cable channels hierarchically, extracts the technical characteristics of electrical engineering automation control, integrates the equipment operation information, builds the automatic distribution linkage model, mines the data rules of power index, sets the distribution structure of electrical equipment by big data clustering algorithm, and centrally configures the functional units.Experimental Results. Compared with the other two control methods, the average noise of this control method is 19.774 dB, 35.462 dB, and 36.323 dB, which proves that the control method combined with big data clustering algorithm has better practical application effect.

Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

The objective was to identify previously unknown groups in a dataset using various techniques. Significant progress has been made in this field in recent years, resulting in the development of novel and promising clustering algorithms. With the constant advancement of big data technology, research on study tours has also become crucial. Clustering can unearth the potential hidden information in large datasets, thereby facilitating more efficient work. Diverse measures have been proposed to quantify similarity, including the Euclidean distance and data space density. As a result, clustering becomes a multi-objective optimization problem. Clustering algorithms are extensively utilized in data preprocessing, data classification, and big data prediction. In this study, we examine clustering methods for big data from a theoretical perspective to comprehend their correlations across a large number of datasets. In addition, we predicted customer demand for research products using fabricated metrics.

Evaluation of English Proficiency Based on Big Data Clustering Algorithm

As one of the common languages in the world, English is playing a more and more important role in trade exchanges, cultural exchanges, and other transnational cooperation. In order to better integrate into the world and communicate with the world, China began to vigorously expand English learning as early as the late 1980s. However, with the development trend of economic globalization and cultural diversity, the traditional English ability evaluation methods are not enough to comprehensively measure individual English ability. Therefore, based on this background, this paper introduces the clustering algorithm into English ability evaluation, improves the traditional clustering algorithm represented by K -means on the basis of predecessors, and captures the evaluation data through neural network algorithm combined with the background of modern big data framework. The results show that the fuzzy logic feature mapping clustering algorithm (FLFM) proposed in this paper performs better than the traditional evaluation algorithm, and the evaluation effectiveness and comprehensiveness are improved by about 10 pt.

DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm is nearly 70% higher than that of DBSCAN, and it is still very close to the efficiency of the BIRCH algorithm. The algorithm has a high processing speed for large-scale data sets without increasing the time complexity and can also accurately cluster clusters of any shape. When the data set increases from 9000 rows to 58000 rows, in turn, the time-consuming of the traditional DBSCAN algorithm increases sharply, while the time-consuming of the improved DBSCAN algorithm is still stable, and the time-consuming gap between the two is getting bigger and bigger. At the same time, the algorithm adopts a heuristic adaptive algorithm to estimate some threshold parameters of the clustering algorithm, which can avoid the direct setting of the threshold parameters by the user and can effectively estimate the relevant threshold parameters, extract clusters of any shape, and the clustering effect is obvious.

Research and Application of Clustering Algorithm for Text Big Data.

In the era of big data, text as an information reserve database is very important, in all walks of life. From humanities research to government decision-making, from precision medicine to quantitative finance, from customer management to marketing, massive text, as one of the most important information carriers, plays an important role everywhere. The text data generated in these practical problems of humanities research, financial industry, marketing, and other fields often has obvious domain characteristics, often containing the professional vocabulary and unique language patterns in these fields and often accompanied by a variety of “noise.” Dealing with such texts is a great challenge for the current technical conditions, especially for Chinese texts. A clustering algorithm provides a better solution for text big data information processing. Clustering algorithm is the main body of cluster analysis, K-means algorithm with its implementation principle is simple, low time complexity is widely used in the field of cluster analysis, but its K value needs to be preset, initial clustering center random selection into local optimal solution, other clustering algorithm, such as mean drift clustering, K-means clustering in mining text big data. In view of the problems of the above algorithm, this paper first extracts and analyzes the text big data and then does experiments with the clustering algorithm. Experimental conclusion: by analyzing large-scale text data limited to large-scale and simple data set, the traditional K-means algorithm has low efficiency and reduced accuracy, and the K-means algorithm is susceptible to the influence of initial center and abnormal data. According to the above problems, the K-means cluster analysis algorithm for data sets with large data volumes is analyzed and improved to improve its execution efficiency and accuracy on data sets with large data volume set. Mean shift clustering can be regarded as making many random centers move towards the direction of maximum density gradually, that is, moving their mean centroid continuously according to the probability density of data and finally obtaining multiple maximum density centers. It can also be said that mean shift clustering is a kernel density estimation algorithm.

Countermeasure of Telecom Network Fraud Investigation Based on Big Data

With the diversification of information data in the information age of big data and the integration of network technology and the development of different industries, criminals who carry out telecommunication fraud are also using the technical loopholes existing in the process of integration of big data with different industries as an opportunity to commit crimes. This paper studies the investigation process and countermeasures of telecom network fraud through big data technology. This paper first introduces the characteristics of big data, analyzes the challenge of personal information security under the background of big data, warns people to protect their personal information in the era of big data, puts forward the clustering algorithm based on big data, introduces the concrete steps based on big data clustering algorithm, and then puts forward the specific steps of big data clustering algorithm. The current situation of telecom network fraud is analyzed, and the telecommunication network fraud is clustered based on big data. The experimental results show that, based on the clustering analysis of telecommunication network fraud based on big data, it is found that through the information age of big data, as long as big data are used rationally, it can effectively suppress telecommunications fraud and reduce it by 80%.