检索结果-内蒙古大学图书馆

JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第3期28卷 469-477页

作者： Kong, Fancai Zhengzhou Univ Sci & Technol Sch Business Adm Zhengzhou 450000 Peoples R China

big data clustering plays an important role in the field of business administration data processing, but the big data clustering method has some problems such as poor clustering effect and low Jaccard coefficient. Therefore, this paper proposes a novel business administration big data clustering optimization method based on active density peak and Salpa swarm algorithm. This method combines principal component analysis method and information entropy dimensionality reduction to process big data, reduces the time required for data clustering, and uses intuitionistic fuzzy kernel clustering algorithm to cluster big data. The algorithm uses the Logic-Tent chaotic sequence to promote the uniform distribution of the initial population of Salp swarm algorithm (SSA). By adding attenuation factors that limit the leader's over-searching, the global exploration ability and local exploitation ability of the algorithm are balanced. Adaptive inertia weights are added to follower updates to avoid convergence of the algorithm to local extreme values. This method designs a fast update strategy and effectively updates labels. An active cluster integration framework combining local and global uncertainties is proposed, and weighted voting consistency method is introduced to optimize the integration of cluster results. Experimental results on data sets show that the proposed method can effectively solve the problems of traditional methods and has good clustering performance.

关键词： big data clustering active density peak Salpa swarm algorithm fast update strategy adaptive inertia weight

来源：评论

学校读者我要写书评

暂无评论

Student Psychology based optimized routing algorithm for big data clustering in IoT with MapReduce framework

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 2051-2063页

作者： Shanmugam, Gowri Thanarajan, Tamilvizhi Rajendran, Surendran Murugaraj, Sadish Sendil Sathyabama Inst Sci & Technol Sch Comp Chennai Tamil Nadu India Panimalar Engn Coll Dept Comp Sci & Engn Chennai 600123 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Guru Nanak Inst Technol Dept Emerging Technol Ibrahipatnam Telangana India

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 InvasiveWeed 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.

关键词： Internet of Things routing big data big data clustering student psychology based optimization

来源：评论

学校读者我要写书评

暂无评论

big data clustering Optimization Based on Intuitionistic Fuzzy Set Distance and Particle Swarm Optimization forWireless Sensor Networks

IJLAI Transactions on Science and Engineering

引用

IJLAI Transactions on Science and Engineering 2024年第3期2卷 26-35页

作者： Ye Li Tianbao Shang Shengxiao Gao Software College Shenyang Normal University Shenyang 110034 China

big data clustering plays an important role in the field of data processing in wireless sensor ***,there are some problems such as poor clustering effect and low Jaccard *** paper proposes a novel big data clustering optimization method based on intuitionistic fuzzy set distance and particle swarm optimization for wireless sensor *** method combines principal component analysis method and information entropy dimensionality reduction to process big data and reduce the time required for data clustering.A new distance measurement method of intuitionistic fuzzy sets is defined,which not only considers membership and non-membership information,but also considers the allocation of hesitancy to membership and non-membership,thereby indirectly introducing hesitancy into intuitionistic fuzzy set *** intuitionistic fuzzy kernel clustering algorithm is used to cluster big data,and particle swarm optimization is introduced to optimize the intuitionistic fuzzy kernel clustering *** optimized algorithm is used to obtain the optimization results of wireless sensor network big data clustering,and the big data clustering is *** results show that the proposed method has good clustering effect by comparing with other state-of-the-art clustering methods.

关键词： big data clustering Intuitionistic fuzzy set distance Particle swarm optimization Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

PPHOPCM: Privacy-Preserving High-Order Possibilistic c-Means Algorithm for big data clustering with Cloud Computing

引用

IEEE TRANSACTIONS ON big data 2022年第1期8卷 25-34页

作者： Zhang, Qingchen Yang, Laurence T. Chen, Zhikui Li, Peng Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada Dalian Univ Technol Sch Software Technol Dalian 116023 Ganjingzi Peoples R China

As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogenous data, since it is initially designed for only small structured dataset. To tackle this problem, the paper proposes a high-order PCM algorithm (HOPCM) for big data clustering by optimizing the objective function in the tensor space. Further, we design a distributed HOPCM method based on MapReduce for very large amounts of heterogeneous data. Finally, we devise a privacy-preserving HOPCM algorithm (PPHOPCM) to protect the private data on cloud by applying the BGV encryption scheme to HOPCM, In PPHOPCM, the functions for updating the membership matrix and clustering centers are approximated as polynomial functions to support the secure computing of the BGV scheme. Experimental results indicate that PPHOPCM can effectively cluster a large number of heterogeneous data using cloud computing without disclosure of private data.

关键词： big data clustering cloud computing privacy preserving possibilistic c-means tensor space

来源：评论

学校读者我要写书评

暂无评论

Design of Financial and Economic Monitoring System Based on big data clustering 6th

Design of Financial and Economic Monitoring System Based on ...

引用

6th European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Wang, Kaili Chen, Yu Nantong Inst Technol Sch Business Nantong 226000 Peoples R China

ISBN: (纸本)9783031287862;9783031287879

Aiming at the problem that the selection of financial and economic risk indicators is not comprehensive, resulting in excessive financial and economic operational risks, a financial and economic monitoring system is designed based on big data clustering. Financial and economic risks are risks that are formed and accumulated in the financial system in the process of economic cyclical and financial unbalanced development. According to its formation mechanism, complex network models are used to analyze the dynamic correlation of financial and economic risks. Select financial and economic risk indicators based on big data clustering, and strengthen the supervision of high-risk financial sub-markets such as stocks and foreign exchange markets. Establish a financial and economic monitoring system from three aspects of financial and economic revenue and expenditure, debt and external risks, and jointly determine the development trend of financial and economic risks. The test results show that the financial and economic monitoring system based on big data clustering can reduce the value at risk of operational risk, namely VaR value, and promote the balanced development of the financial market.

关键词： big data clustering Financial economy Monitoring system Financial risk Financial network Dynamic correlation

来源：评论

学校读者我要写书评

暂无评论

Deep FC-IIWO Fuzzy clustering: An Optimized Fuzzy clustering Approach for big data clustering

引用

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2025年第03期33卷 329-352页

作者： Sudha, D. Gowri, S. Sathyabama Inst Sci & Tecnol Dept CSE Chennai 600129 Tamilnadu India Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai 600119 Tamilnadu India

A significant role is played by clustering approaches in data mining processes, which becomes more challenging because of the rising dimension of the available databases. clustering strategies are employed in various sectors like information retrieval, social network analytics, image processing, and so on. clustering assists the user to understand dissimilarity and similarity among objects. The big data concept has collected incredible attention from various research areas, like government and industry within a short lifetime. In this paper, a Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) based scheme is developed to cluster big data. The map-reduce architecture is employed to tackle the over-fitting obstacles occurring in the clustering of big data. The features are extracted using the mapping function which is followed by clustering in the reducer stage. Furthermore, the selection of features is essential for further clustering process. Here, the Minkowski distance measure is applied for selecting the most prominent features. The approach of clustering is executed with the help of the devised Deep FC-IIWO fuzzy clustering model. The implemented FC-IIWO framework is newly created by integrating Fractional Calculus (FC) and Improved Invasive Weed Optimizer (IIWO). The proposed Deep FC-IIWO fuzzy clustering scheme performed better than other prevailing models concerning the rand coefficient, Jaccard coefficient, and clustering accuracy of 0.9563, 0.9379, and 0.9522, correspondingly.

关键词： Improved invasive weed optimizer big data clustering fuzzy clustering minkowski distance fractional calculus

来源：评论

学校读者我要写书评

暂无评论

Improvement of K-Means Algorithm for Accelerated big data clustering

引用

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2021年第2期14卷 99-119页

作者： Wu, Chunqiong Yan, Bingwen Yu, Rongrui Huang, Zhangshu Yu, Baoqin Yu, Yanliang Chen, Na Zhou, Xiukao Yango Univ Business Coll Fuzhou Peoples R China Fujian Univ Big Data Business Intelligence Engn Res Ctr Fuzhou Peoples R China

With the rapid development of the computer level, especially in recent years, "Internet +," cloud platforms, etc. have been used in various industries, and various types of data have grown in large quantities. Behind these large amounts of data often contain very rich information, relying on traditional data retrieval and analysis methods, and data management models can no longer meet our needs for data acquisition and management. Therefore, data mining technology has become one of the solutions to how to quickly obtain useful information in today's society. Effectively processing large-scale data clustering is one of the important research directions in data mining. The k-means algorithm is the simplest and most basic method in processing large-scale data clustering. The k-means algorithm has the advantages of simple operation, fast speed, and good scalability in processing large data, but it also often exposes fatal defects in data processing. In view of some defects exposed by the traditional k-means algorithm, this paper mainly improves and analyzes from two aspects.

关键词： big data clustering clustering Algorithms K-Means Algorithm

来源：评论

学校读者我要写书评

暂无评论

Min-max kurtosis mean distance based k-means initial centroid initialization method for big genomic data clustering

引用

EVOLUTIONARY INTELLIGENCE 2023年第3期16卷 1055-1076页

作者： Pandey, Kamlesh Kumar Shukla, Diwakar Dr Hari Singh Gour Vishwavidyalaya Dept Comp Sci & Applicat Sagar Madhya Pradesh India

Genomic clustering is a big data application that uses the K-means (KM) clustering approach to discover hidden patterns and trends in genes for disease diagnosis, biological analysis, and tissue detection. The KM algorithm is highly dependent on the initial centroid because it determines the effectiveness, efficiency, computing resources, and local optima of the KM clustering. The existing initial centroid initialization approach traps local optima due to randomization and achieves high computational cost due to the enormous interrelated dimension. Therefore, the KM algorithm produces the lowest quality cluster and maximizes the computation time and resource consumption. To address this issue, this study has presented the Min-Max Kurtosis Mean Distance (MKMD) algorithm for big data clustering in a single machine environment. The MKMD algorithm enhances the effectiveness and efficiency of the KM algorithm by measuring the distance between data points of the minimum-maximum kurtosis dimension and their mean. The performance of the presented algorithm has been compared against KM, KM + + , ADV, MKM, Mean-KM, NFD, K-MAM, NRKM2, FMNN and MuKM algorithms using internal and external effectiveness evaluation criteria with efficiency assessment on sixteen genomic datasets. The experimental results reveal that the MKMDKM algorithm minimizes iterations, distance computation, data comparison, local optima, resource consumption, and improves cluster performance, effectiveness and efficiency with stable convergence and results as compared to other algorithms. According to the statistical analysis, the proposed MKMDKM algorithm has achieved statistical significance by employing the Friedman test and the post hoc test.

关键词： big data clustering Initial centroid algorithm Genome clustering Kurtosis clustering Convergence speed K-Means

来源：评论

学校读者我要写书评

暂无评论

MapReduce framework based big data clustering using fractional integrated sparse fuzzy C means algorithm

引用

IET IMAGE PROCESSING 2020年第12期14卷 2719-2727页

作者： Kulkarni, Omkaresh Jena, Sudarson Ravi Sankar, V. GITAM Univ Hyderabad India Sambalpur Univ SUIIT Sambalpur India

big data analytics gain significant interest over the traditional data-processing methodologies that engage in extracting the hidden patterns and correlations from the massive data, termed as big data. With the aim of relieving the computational complexity the clustering method plays a significant role. With the knowledge of the clustering algorithms, the big data arriving from the distributed sources is processed using the MapReduce framework (MRF). The MRF possesses two functions, namely, map function and reduce function, such that the map function is based on the proposed Fractional Sparse Fuzzy C-Means (FrSparse FCM) algorithm and reduce function is based on particle swarm optimisation-based whale optimisation algorithm (P-Whale). Initially, the optimal centroids are computed using the proposed algorithm in the mapper phase that is optimally tuned in the reducer phase, and it is clear that the proposed FrSparse FCM-based MRF ensures the parallel processing of the big data. Experimentation is performed using the Skin data set and the localisation data set taken from the UCI machine learning repository, and the analysis is progressed using the metrics, such as accuracy and DB Index. The analysis proves that the proposed method acquired a maximum accuracy of 90.6012% and a minimum DB Index of 5.33.

关键词： big data particle swarm optimisation pattern clustering data mining learning (artificial intelligence) fuzzy set theory pattern classification data analysis parallel processing MapReduce framework big data clustering big data analytics massive data clustering algorithms map function particle swarm optimisation-based whale optimisation algorithm FrSparse FCM-based MRF Skin data localisation data fractional sparse fuzzy C-means algorithm

来源：评论

学校读者我要写书评

暂无评论

Moth-Flame Optimization-Bat Optimization: Map-Reduce Framework for big data clustering Using the Moth-Flame Bat Optimization and Sparse Fuzzy C-Means

引用

big data 2020年第3期8卷 203-217页

作者： Ravuri, Vasavi Vasundra, S. VNRVJIET Hyderabad 500090 Telangana India JNTUA Univ Dept CSE Ananthapuramu India JNTUA Univ Ananthapuramu India

The technical advancements in big data have become popular and most desirable among users for storing, processing, and handling huge data sets. However, clustering using these big data sets has become a major challenge in big data analysis. The conventional clustering algorithms used scalable solutions for managing huge data sets. Thus, this study proposes a technique for big data clustering using the spark architecture. The proposed technique undergoes two steps for clustering the big data, involving feature selection and clustering, performed in the initial cluster nodes of spark architecture. At first, the initial cluster nodes read the big data from various distributed systems, and the optimal features are selected and placed in the feature vector based on the proposed moth-flame optimization-based bat (MFO-Bat) algorithm, which is designed by integrating MFO and Bat algorithms. Then, the selected features are fed to the final cluster nodes of spark, which uses the sparse-fuzzy C-means method for performing optimal clustering. The performance of proposed MFO-Bat outperformed other existing methods with a maximal classification accuracy of 95.806%, Dice coefficient of 99.181%, and Jaccard coefficient of 98.376%, respectively.

关键词： big data big data clustering fuzzy optimization algorithm spark architecture

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：