scholarly journals Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ge Zhou
Keyword(s):  
Data Mining ◽  
Mobile Internet ◽  
Distributed Data ◽  
Cloud Platform ◽  
Data Set ◽  
Internet Applications ◽  
Internet Service ◽  
Clustering Quality ◽  
Efficient Data ◽  
Application Aware

Because the static cloud platform cannot satisfy the diversity of mobile Internet service and inefficient data mining problems, we presented a reliable and efficient data mining cloud platform construction scheme based on the mobile Internet service opportunistic driving and application perception. In this scheme, first of all data selection mechanism was established based on mobile Internet service opportunistic drive. Secondly, through the cloud platform different cloud and channel aware, nonlinear mapping from the service to a data set of proposed perceptual model is applied. Finally, on the basis of the driving characteristics and extraction of perceptual features, the cloud platform would be constructed through the service opportunities of mobile Internet applications, which could provide robust and efficient data mining services. The experimental results show that the proposed mechanism, compared to the cloud platform based on distributed data mining, has obvious advantages in system running time, memory usage, and data clustering required time, as well as average clustering quality.

scholarly journals NFDDC: Novel Neuro-Fuzzy Logic based Methodology for Distributed Data Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 5370-5375
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Big Data ◽  
Data Classification ◽  
Classification Model ◽  
Distributed Data ◽  
Data Mining Technique ◽  
Data Set ◽  
Internet Applications ◽  
Neuro Fuzzy

With the growing culture of Internet applications and their usage lead to challenging task for storing a massive volume of high-velocity data from different fields. This result an evolution of big data with integrated, i.e. Volume, Velocity, and Variety (3V's). The voluminous data extraction is a very complex task which is not possible form classical data mining techniques. Therefore, a big data mining technique is introducing by modifying traditional data mining scheme using a novel of Neuro-Fuzzy Logic based approach, i.e. named as NFDDC. The proposed distributed data classification model performs into three stages first- reduce the data set dimension, second- data clustering, and third-data classification using the neuro-fuzzy method. The performance of the NFDDC system is analysed using two different datasets, i.e. medical data and e-commerce datasets. Additionally, comparative analysis is performed by measuring the accuracy of existing CCSA algorithm with proposed NFDDC algorithm and will get 90% accuracy in data classification

Grid-Based Platform for Mining Association Rules

2010 ◽  
Vol 34-35 ◽  
pp. 1961-1965
Author(s):  
You Qu Chang ◽  
Guo Ping Hou ◽  
Huai Yong Deng
Keyword(s):  
Data Mining ◽  
Large Datasets ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Data Set ◽  
Efficiency And Effectiveness ◽  
Geographically Distributed ◽  
Commercial Applications ◽  
Rule Interestingness ◽  
Grid Based

distributed data mining is widely used in industrial and commercial applications to analyze large datasets maintained over geographically distributed sites. This paper discusses the disadvantages of existing distributed data mining systems, and puts forward a distributed data mining platform based grid computing. The experiments done on a data set showed that the proposed approach produces meaningful results and has reasonable efficiency and effectiveness providing a trade-off between runtime and rule interestingness.

Improvement of Association Algorithm and Its Application in Audit Data Mining

2021 ◽  
Author(s):  
Haitao Lu ◽  
C. B. Sivaparthipan ◽  
A. Antonidoss
Keyword(s):  
Data Mining ◽  
Performance Ratio ◽  
Data Set ◽  
Audit Risk ◽  
Audit Data ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Accuracy Ratio ◽  
Efficient Data ◽  
Association Algorithm

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.

scholarly journals Optimization of the Marketing Management System Based on Cloud Computing and Big Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lin Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Rapid Development ◽  
Social Economy ◽  
Mobile Internet ◽  
Attribute Selection ◽  
Marketing System ◽  
Mining Technology ◽  
Internet Applications ◽  
Attribute Analysis

With the rapid development of the Internet information age, social networks, mobile Internet, and e-commerce have expanded the scope of Internet applications. The “big data” era is a challenge and chance for companies and has a great impact on social economy, politics, culture, and people’s lives. An accurate marketing system is developed based on J2EE, and the architecture is selected from the user layer, business logic layer, and data layer and the B/S3 layer application (three-tier application), including three layers of crip-dm and semma. And, other process methods are used. Data-mining-based marketing system information solutions consist of several parts, such as requirement analysis, design, implementation, and testing. This paper introduces data mining technology to the marketing business based on the practical use and design IT solutions for precision marketing, attribute selection tools, attribute analysis tools, modeling prediction tools, and others. This paper introduces a precision marketing system based on data mining technology. The system passes the actual test and the deployment and the operation of this system are confirmed. The system, which can improve marketing activity, is tested, and the development and operation of this system markedly increase the company’s earnings.

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