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

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

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

A hierarchical fuzzy cluster ensemble approach and its application to big data clustering

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第6期28卷 2409-2421页

作者： Su, Pan Shang, Changjing Shen, Qiang Aberystwyth Univ Inst Math Phys & Comp Sci Dept Comp Sci Aberystwyth SY23 3DB Ceredigion Wales

Cluster ensembles organically integrate individual component methods which may utilise different parameter settings and features, and which may themselves be generated on the basis of different representations and learning mechanisms. Such a technique offers an effective means for aggregating multiple clustering results in order to improve the overall clustering accuracy and robustness. Many topics regarding cluster ensembles have been proposed and promising results are gained in the literature. To reinforce such development, this paper presents another cluster ensemble approach for fuzzy clustering, with an aim to be applied for clustering of big data. The proposed algorithm first generates fuzzy base clusters with respect to each data feature and then, employs a fuzzy hierarchical graph to represent the relationships between the resulting base clusters. Whilst the work employs fuzzy c-means and hierarchical clustering in generating base cluster and implementing consensus function respectively, when applied to large datasets it has lower time complexity than the original fuzzy c-means and hierarchical clustering. The resultant ensemble clustering mechanism is tested against traditional clustering methods on various benchmark datasets. Experimental results demonstrate that it generally outperforms crisp cluster ensembles and single linkage agglomerative clustering, in terms of accuracy in conjunction with time efficiency, thereby showing that it has the potential for application in clustering big data.

关键词： Fuzzy cluster ensemble big data clustering fuzzy c-means hierarchical clustering data mining

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

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

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

Fractional Fuzzy clustering and Particle Whale Optimization-Based MapReduce Framework for big data clustering

引用

JOURNAL OF INTELLIGENT SYSTEMS 2020年第1期29卷 1496-1513页

作者： Kulkarni, Omkaresh Jena, Sudarson Sanjay, C. H. GITAM Univ Gandhi Inst Technol & Management Hyderabad 502329 Telangana India Sambalpur Univ Inst Informat Technol Dept Comp Sci Engn & Applicat Sambalpur Orissa India GITAM Univ Hyderabad India

The recent advancements in information technology and the web tend to increase the volume of data used in day-to-day life. The result is a big data era, which has become a key issue in research due to the complexity in the analysis of big data. This paper presents a technique called FPWhale-MRF for big data clustering using the MapReduce framework (MRF), by proposing two clustering algorithms. In FPWhale-MRF, the mapper function estimates the cluster centroids using the Fractional Tangential-Spherical Kernel clustering algorithm, which is developed by integrating the fractional theory into a Tangential-Spherical Kernel clustering approach. The reducer combines the mapper outputs to find the optimal centroids using the proposed Particle-Whale (P-Whale) algorithm, for the clustering. The P-Whale algorithm is proposed by combining Whale Optimization Algorithm with Particle Swarm Optimization, for effective clustering such that its performance is improved. Two datasets, namely localization and skin segmentation datasets, are used for the experimentation and the performance is evaluated regarding two performance evaluation metrics: clustering accuracy and DB-index. The maximum accuracy attained by the proposed FPWhale-MRF technique is 87.91% and 90% for the localization and skin segmentation datasets, respectively, thus proving its effectiveness in big data clustering.

关键词： big data clustering fractional theory TSK clustering MRF PSO WOA

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

big data clustering techniques based on Spark: a literature review

引用

PEERJ COMPUTER SCIENCE 2020年 6卷 e321页

作者： Saeed, Mozamel M. Al Aghbari, Zaher Alsharidah, Mohammed Prince Sattam Bin Abdulaziz Univ Dept Comp Sci Riyadh Saudi Arabia Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

A popular unsupervised learning method, known as clustering, is extensively used in data mining, machine learning and pattern recognition. The procedure involves grouping of single and distinct points in a group in such a way that they are either similar to each other or dissimilar to points of other clusters. Traditional clustering methods are greatly challenged by the recent massive growth of data. Therefore, several research works proposed novel designs for clustering methods that leverage the benefits of big data platforms, such as Apache Spark, which is designed for fast and distributed massive data processing. However, Spark-based clustering research is still in its early days. In this systematic survey, we investigate the existing Spark-based clustering methods in terms of their support to the characteristics big data. Moreover, we propose a new taxonomy for the Spark-based clustering methods. To the best of our knowledge, no survey has been conducted on Spark-based clustering of big data. Therefore, this survey aims to present a comprehensive summary of the previous studies in the field of big data clustering using Apache Spark during the span of 2010-2020. This survey also highlights the new research directions in the field of clustering massive data.

关键词： Spark-based clustering big data clustering Spark big data

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：