scholarly journals Fine clustering analysis of Internet financial credit investigation based on big data

2021 ◽  
pp. 100297
Author(s):  
Jingqi Sun ◽  
Yu Li ◽  
Qiang Li ◽  
Yingji Li ◽  
Yanshu Jia ◽  
...  
Keyword(s):  
Big Data ◽  
Clustering Analysis ◽  
Financial Credit

Precise clustering analysis of Internet financial credit reporting dependent on multidimensional attribute sparse large data

2021 ◽  
pp. 002072092110020
Author(s):  
Lingling Chen ◽  
Yuanyuan Zhang ◽  
Min Zeng
Keyword(s):  
Clustering Analysis ◽  
Large Data ◽  
Distance Matrix ◽  
The Internet ◽  
Relationship Matrix ◽  
Clustering Methods ◽  
Correlation Relationship ◽  
Credit Reporting ◽  
Financial Credit ◽  
Approximate Distance

Given that the traditional methods cannot perform clustering analysis on the Internet financial credit reporting directly and effectively, a kind of precise clustering analysis of internet financial credit reporting dependent on multidimensional attribute sparse large data is proposed. By measuring the overall distance between Internet financial credit reporting through the sparse large data with multidimensional attributes, the multidimensional attribute sparse large data are used to perform clustering analysis on the overall distance matrix and the component approximate distance matrix between the data, respectively. The correlation relationship between the Internet financial credit reporting under these two perspectives is taken into comprehensive consideration. Multidimensional attribute sparse large data pairs are used to reflect the comprehensive relationship matrix of the original Internet financial credit reporting to achieve clustering with relatively high quality. Numerical experiments show that compared with the traditional clustering methods, the method proposed in this paper can not only reflect the overall data features effectively, but also improve the clustering effect of the original Internet financial credit reporting data through the analysis of the correlation relationship between the important component attribute sequences.

scholarly journals Give us a little social credit: to design or to discover personal ratings in the era of Big Data

2020 ◽  
Vol 16 (3) ◽  
pp. 369-387 ◽  
Author(s):  
Abigail Devereaux ◽  
Linan Peng
Keyword(s):  
Big Data ◽  
Credit Rating ◽  
Credit Ratings ◽  
Personal Characteristics ◽  
The State ◽  
State Council ◽  
Credit System ◽  
Personal Credit ◽  
Social Credit ◽  
Financial Credit

AbstractIn 2014, the State Council of the Chinese Communist Party announced the institution of a social credit system by 2020, a follow-up to a similar statement on the creation of a social credit system issued by the State Council in 2007. Social credit ratings of the type being developed by the State Council in partnership with Chinese companies go beyond existing financial credit ratings in an attempt to project less-tangible personal characteristics like trustworthiness, criminal tendencies, and group loyalty onto a single scale. The emergence of personal credit ratings is enabled by Big Data, automated decision-making processes, machine learning, and facial recognition technology. It is quite likely that various kinds of personal and social credit ratings shall become reality in the near future. We explore China's version of its social credit system so far, compare the welfare and epistemological qualities of an ecology of personal ratings emanating from polycentric sources versus a social credit rating, and discuss whether a social credit system in an ideologically driven state is less a tool to maximize social welfare through trustworthiness provision and more a method of preventing and punishing deviance from a set of party-held ideological values.

On the Effectiveness of Hybrid Canopy with Hoeffding Adaptive Naive Bayes Trees

2017 ◽  
Vol 8 (2) ◽  
pp. 30-43
Author(s):  
Mrutyunjaya Panda
Keyword(s):  
Big Data ◽  
Clustering Analysis ◽  
Large Scale ◽  
Data Sets ◽  
Recent Past ◽  
Large Scale Data ◽  
Huge Data ◽  
With Memory ◽  
Memory Constraints ◽  
Scale Data

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be solved by using parallel computing techniques, where a single machine or a multiple machine can perform the work simultaneously, dividing the problem into sub problems and assigning some private memory to each sub problems. Clustering analysis are found to be useful in handling such a huge data in the recent past. Even though, there are many investigations in Big data analysis are on, still, to solve this issue, Canopy and K-Means++ clustering are used for processing the large-scale data in shorter amount of time with no memory constraints. In order to find the suitability of the approach, several data sets are considered ranging from small to very large ones having diverse filed of applications. The experimental results opine that the proposed approach is fast and accurate.

scholarly journals Comparisons of the City Brand Influence of Global Cities: Word-Embedding Based Semantic Mining and Clustering Analysis on the Big Data of GDELT Global News Knowledge Graph

2020 ◽  
Vol 12 (16) ◽  
pp. 6294
Author(s):  
Chenyu Zheng
Keyword(s):  
Big Data ◽  
News Media ◽  
Clustering Analysis ◽  
Global City ◽  
Word Embedding ◽  
Knowledge Graph ◽  
Global Cities ◽  
City Brand ◽  
Semantic Mining ◽  
The City

Global cities act as influential hubs in the networked world. Their city brands, which are projected by the global news media, are becoming sustainable resources in various global competitions and cooperations. This study adopts the research paradigm of computational social science to assess and compare the city brand attention, positivity, and influence of ten Globalization and World Cities Research Network (GaWC) Alpha+ global cities, along with their dimensional structures, based on combining the cognitive and affective theoretical perspectives on the frameworks of the Anholt global city brand dimension system, the big data of global news knowledge graph in Google’s Global Database of Events, Language, and Tone (GDELT), and the technologies of word-embedding semantic mining and clustering analysis. The empirical results show that the overall values and dimensional structures of city brand influence of global cities form distinct levels and clusters, respectively. Although global cities share a common structural characteristic of city brand influence of the dimensions of presence and potential being most prominent, Western and Eastern global cities differentiate in the clustering of dimensional structures of city brand attention, positivity, and influence. City brand attention is more important than city brand positivity in improving the city brand influence of global cities. The preferences of the global news media over global city brands fits the nature of global cities.

Big Data Clustering Analysis Algorithm for Internet of Things Based on K-Means

2019 ◽  
Vol 10 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Zhanqiu Yu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Prototype System ◽  
Point Selection ◽  
Logistics System ◽  
Relational Schema ◽  
Analysis Algorithm

To explore the Internet of things logistics system application, an Internet of things big data clustering analysis algorithm based on K-mans was discussed. First of all, according to the complex event relation and processing technology, the big data processing of Internet of things was transformed into the extraction and analysis of complex relational schema, so as to provide support for simplifying the processing complexity of big data in Internet of things (IOT). The traditional K-means algorithm was optimized and improved to make it fit the demand of big data RFID data network. Based on Hadoop cloud cluster platform, a K-means cluster analysis was achieved. In addition, based on the traditional clustering algorithm, a center point selection technology suitable for RFID IOT data clustering was selected. The results showed that the clustering efficiency was improved to some extent. As a result, an RFID Internet of things clustering analysis prototype system is designed and realized, which further tests the feasibility.

scholarly journals Research on Clustering Analysis of Big Data

2012 ◽  
Vol 6-7 ◽  
pp. 82-87 ◽  
Author(s):  
Yuan Ming Yuan ◽  
Chan Le Wu
Keyword(s):  
Big Data ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Mapreduce Framework ◽  
Traditional Technologies

Data quantity of Big Data was too big to be processed with traditional clustering analysis technologies. Time consuming was long, problem of computability existed with traditional technologies. Having analyzed on k-means clustering algorithm, a new algorithm was proposed. Parallelizing part of k-means was found. The algorithm was improved with the method of redesigning flow with MapReduce framework. Problems mentioned above were solved. Experiments show that new algorithm is feasible and effective.

scholarly journals Challenges of Credit Reference Based on Big Data Technology in China

2021 ◽  
Author(s):  
Cheng-yong Liu ◽  
Chih-Chun Hou
Keyword(s):  
Big Data ◽  
Information Security ◽  
Secure Communication ◽  
Distributed Storage ◽  
Blockchain Technology ◽  
Financial Credit ◽  
Credit Data ◽  
Point To Point ◽  
The Individual ◽  
Big Data Technology

AbstractBig data-based credit reference system gradually attracts wide attention due to its ad-vantages in remedying the shortages of traditional credit reference and dealing with new challenges arising from financial credit management. Nevertheless, this new method is also adapted through different studies and experiments to be problematic with island of credit information and information security. Some researchers begin exploring the possibility of applying blockchain technology to the individual credit reference field. The business links in the individual credit reference can be innovated through the blockchain mechanism so that credit data from different industries get collected through peering points, secure communication and anonymous protection on the basis of such techniques as distributed storage, point-to-point transmission, consensus mechanism and encryption algorithm. In this way, it is feasible to solve island of information and enhance the protection of user information security. A promising future can be expected about the big data-based credit reference, but there are also many problems with blockchain-based credit reference in China.

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