Data mining and statistics issues of precision and intelligent agriculture based on big data analysis

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”.

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

Security and Communication Networks ◽

10.1155/2021/5545827 ◽

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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Big data analysis on the business process and management for the store layout and bundling sales

Business Process Management Journal ◽

10.1108/bpmj-01-2018-0027 ◽

2019 ◽

Vol 25 (7) ◽

pp. 1783-1801 ◽

Cited By ~ 1

Author(s):

Shu-hsien Liao ◽

Yi-Shan Tasi

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

Business Process ◽

Process Management ◽

Business Processes ◽

Analysis Data ◽

Database System ◽

Big Data Analysis ◽

Content Type

Purpose In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue. Design/methodology/approach This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system. Findings Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development. Originality/value This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.

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A Big Data-Based Data Mining Tool for Physical Education and Technical and Tactical Analysis

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v14i22.11345 ◽

2019 ◽

Vol 14 (22) ◽

pp. 220 ◽

Cited By ~ 1

Author(s):

Lili Pan

Keyword(s):

Data Mining ◽

Information Technology ◽

Big Data ◽

Data Analysis ◽

Physical Education ◽

Big Data Analysis ◽

Competitive Sports ◽

Data Mining Tool ◽

Research Findings ◽

Mining Tool

This paper attempts to develop a data mining tool to guide sports training, promote physical education and facilitate technical and tactical analysis. For this purpose, information techniques like mathematical statistics and big data analysis were employed to collect and analyse the information on competitive sports. Based on database and computer algorithm, the author designed a data mining tool applicable to the information of various competitive sports. The proposed tool can mine out valuable information from the big data, enabling trainers to realize targeted and efficient physical education. The mined information also helps improve the analysis of techniques and tactics of competitive sports. The research findings promote the application of information technology in physical education and competitive sports.

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Big Data Analysis and Implementation in Different Areas Using IoT

International Journal of Hyperconnectivity and the Internet of Things ◽

10.4018/ijhiot.2017070102 ◽

2017 ◽

Vol 1 (2) ◽

pp. 12-25

Author(s):

Aqeel ur Rehman ◽

Muhammad Fahad ◽

Rafi Ullah ◽

Faisal Abdullah

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Healthcare Applications ◽

New Techniques ◽

Standard Data ◽

Huge Data ◽

Data Rates ◽

Definition Of

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

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Big Data Analysis for Trend Recognition Using Machine Learning Techniques

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666200304141238 ◽

2020 ◽

Vol 10 (4) ◽

pp. 540-550

Author(s):

Cerene Mariam Abraham ◽

Mannathazhathu Sudheep Elayidom ◽

Thankappan Santhanakrishnan

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Research Work ◽

Big Data Analytics ◽

Big Data Analysis ◽

Machine Learning Techniques ◽

Derivative Market

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis. Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data. Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

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Cloud Computing and Big Data

Pervasive Cloud Computing Technologies - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

10.4018/978-1-4666-4683-4.ch006 ◽

2014 ◽

pp. 118-132

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Big Data ◽

Data Analysis ◽

Association Rules ◽

Big Data Analysis ◽

Center Problem ◽

Privacy Concerns ◽

Numerical Examples ◽

Big Data Mining

This chapter aims at exploring the intersection of cloud computing with big data. The big data analysis, mining, and privacy concerns are discussed. First, this chapter deals with the software framework, MapReduce™ that is commonly used for performing Big Data Analysis in the clouds. In addition, some of the most used techniques for performing Big Data Mining are detailed. For instance, Clustering, Co-Clustering, and Association Rules are described in detail. In particular, the k-center problem is described while with reference to the association rules beyond the basic definitions, the Apriori Algorithm is outlined and illustrated by some numerical examples. These techniques are also described with reference to their versions based on MapReduce. Finally, the description of some real applications conclude the chapter.

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