Performance analysis of clustering algorithm under two kinds of big data architecture

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

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Research on the university intelligent learning analysis system based on AI

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189820 ◽

2021 ◽

pp. 1-10

Author(s):

Meng Huang ◽

Shuai Liu ◽

Yahao Zhang ◽

Kewei Cui ◽

Yana Wen

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Academic Performance ◽

Clustering Algorithm ◽

Back Propagation ◽

Three Dimensional ◽

Training Model ◽

Future Trend ◽

Artificial Intelligence Technology ◽

Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

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Improved K-Means Clustering Algorithm for Big Data Mining under Hadoop Parallel Framework

Journal of Grid Computing ◽

10.1007/s10723-019-09503-0 ◽

2019 ◽

Vol 18 (2) ◽

pp. 239-250 ◽

Cited By ~ 3

Author(s):

Weijia Lu

Keyword(s):

Data Mining ◽

Big Data ◽

Clustering Algorithm ◽

Big Data Mining

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Performance analysis model for big data applications in cloud computing

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-014-0019-z ◽

2014 ◽

Vol 3 (1) ◽

Cited By ~ 8

Author(s):

Luis Eduardo Bautista Villalpando ◽

Alain April ◽

Alain Abran

Keyword(s):

Cloud Computing ◽

Big Data ◽

Performance Analysis ◽

Analysis Model ◽

Big Data Applications

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Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

Web Intelligence ◽

10.3233/web-210463 ◽

2021 ◽

pp. 1-12

Author(s):

Li Qian

Keyword(s):

Big Data ◽

Fuzzy Clustering ◽

Classification Accuracy ◽

Clustering Algorithm ◽

Principal Component ◽

Data Classification ◽

Fisher Discriminant Analysis ◽

Fuzzy Clustering Algorithm ◽

Local Fisher Discriminant Analysis ◽

Big Data Classification

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

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