4th International conference on Big Data Analysis and Data Mining

2017 ◽  
Vol 8 (3) ◽  
pp. 83
Author(s):  
Editorial Board
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Main Theme ◽  
International Conference ◽  
Poster Presentations ◽  
Future Technologies ◽  
Oral Presentations

Conference Series LLC welcomes you to attend the 4th International conference onBig Data Analysis and Data Mining during September 07-08, 2017 in Paris, France.We cordially invite all the participants to share their knowledge and research in the field of Big Data Analysis and Data Mining. Data Mining 2017 anticipates more than 200 participants around the globe with thought provoking Keynote lectures, Oral presentations, Symposiums, Workshops and Poster presentations. The attending delegates include Editorial Board Members of related International Journals. This is an excellent opportunity for the delegates from universities and institutes to interact with the world class scientists and eminent personalities. The intending participants can confirm their participation by registering for the conference along with your colleagues. The main theme of the conference is “Future Technologies for Knowledge Discoveries in Data”.

A Smart Cloud-Based Energy Data Mining Agent Using Big Data Analysis Technology

Smart Science ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 175-183 ◽  
Author(s):  
Hsueh-Yuan Lin ◽  
Sheng-Yuan Yang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Energy Data

scholarly journals Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jui-Chan Huang ◽  
Po-Chang Ko ◽  
Cher-Min Fong ◽  
Sn-Man Lai ◽  
Hsin-Hung Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Credit Risk ◽  
Customer Loyalty ◽  
Online Shopping ◽  
Big Data Analysis ◽  
Web Page ◽  
The Web

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.

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