scholarly journals Model of Cloud-Based Services for Data Mining Analysis

2015 ◽  
Vol 8 (4) ◽  
pp. 40
Author(s):  
Aleksandar Karadimce
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Storage ◽  
Data Mining Algorithm ◽  
Analysis Model ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Major Benefit ◽  
Efficient Execution ◽  
Data Mining Analysis

<p class="zhengwen"><span lang="EN-GB">New cloud-based services are being developed constantly in order to meet the need for faster, reliable and scalable methods for knowledge discovery. The major benefit of the cloud-based services is the efficient execution of heavy computation algorithms in the cloud simply by using Big Data storage and processing platforms. Therefore, we have proposed a model that provides data mining techniques as cloud-based services that are available to users on their demand. The widely known data mining algorithms have been implemented as Map/Reduce jobs that are been executed as services in cloud architecture. The user simply chooses or uploads the dataset to the cloud, makes appropriate settings for the data mining algorithm, executes the job request to be processed and receives the results. The major benefit of this model of cloud-based services is the efficient execution of heavy computation data mining algorithm in the cloud simply by using the Ankus - Open Source Big Data Mining Tool and StarfishHadoop Log Analyzer. The expected outcome of this research is to offer the integration of the cloud-based services for data mining analysis in order to provide researchers with reliable collaborative data mining analysis model.<strong></strong></span></p>

scholarly journals Data Mining Algorithm for Physical Health Monitoring of Young Students Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiangang Sun ◽  
Xiaoran Jiang ◽  
Guoliang Yuan ◽  
Zhenhuai Chen
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Physical Health ◽  
Data Management System ◽  
Practical Significance ◽  
Data Mining Algorithm ◽  
Physical Test ◽  
Healthy Development ◽  
Data Mining Algorithms ◽  
Mining Algorithm

With the continuous improvement of living standards, the level of physical development of adolescents has improved significantly. The physical functions and healthy development of adolescents are relatively slow and even appear to decline. This paper proposes a novel data mining algorithm based on big data for monitoring of adolescent student’s physical health to overcome this problem and enhance young people’s physical fitness and mental health. Since big data technology has positive practical significance in promoting young people’s healthy development and promoting individual health rights, this article will implement commonly used data mining algorithms and Hadoop/Spark big data processing. The algorithm on different platforms verified that the big data platform has good computing performance for the data mining algorithm by comparing the running time. The current work will prove to be a complete physical health data management system and effectively save, process, and analyze adolescents’ physical test data.

scholarly journals Multimedia Computer-Aided Industrial System Design Based on the Study of Big Data Mining Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhihao Zeng
Keyword(s):  
Data Mining ◽  
Big Data ◽  
System Design ◽  
Data Mining Algorithm ◽  
Industrial System ◽  
Overall Design ◽  
Big Data Mining ◽  
Mining Algorithm ◽  
Computer Aided ◽  
The Impact

Aiming at the problems of the multimedia computer-aided industrial system, this paper puts forward the application of big data mining algorithm to multimedia computer-aided industrial system design and analyzes in detail the impact of multimedia technology on industrial quality. This paper introduces the advantages of using big data mining algorithm in multimedia computer technology course, shows the operating environment to be met by using the multimedia computer-aided industrial system, follows the guiding principles of the overall design learning theory and artistic conception cognition theory, supplements specific industrial examples, and discusses multimedia industrial design.

scholarly journals Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Kai Ji
Keyword(s):  
Data Mining ◽  
Communication Network ◽  
Big Data ◽  
Clustering Algorithm ◽  
Personal Information ◽  
Personal Communication ◽  
Communication Process ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Wireless Personal Communication

Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.

scholarly journals Migrating From Data Mining to Big Data Mining

2018 ◽  
Vol 7 (3.4) ◽  
pp. 13
Author(s):  
Gourav Bathla ◽  
Himanshu Aggarwal ◽  
Rinkle Rani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Response Time ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Big Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with  pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples.  Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.  

Comprehensibility of Data Mining Algorithms

2011 ◽  
pp. 190-195 ◽  
Author(s):  
Zhi-Hua Zhou
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Makers ◽  
Data Mining Algorithm ◽  
Human Beings ◽  
Common People ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Aid Decision ◽  
Mining Algorithms

Data mining attempts to identify valid, novel, potentially useful, and ultimately understandable patterns from huge volume of data. The mined patterns must be ultimately understandable because the purpose of data mining is to aid decision-making. If the decision-makers cannot understand what does a mined pattern mean, then the pattern cannot be used well. Since most decision-makers are not data mining experts, ideally, the patterns should be in a style comprehensible to common people. So, comprehensibility of data mining algorithms, that is, the ability of a data mining algorithm to produce patterns understandable to human beings, is an important factor.

TRADE-OFF BETWEEN COMPUTATION TIME AND NUMBER OF RULES FOR FUZZY MINING FROM QUANTITATIVE DATA

2001 ◽  
Vol 09 (05) ◽  
pp. 587-604 ◽  
Author(s):  
TZUNG-PEI HONG ◽  
CHAN-SHENG KUO ◽  
SHENG-CHAI CHI
Keyword(s):  
Data Mining ◽  
Computation Time ◽  
Data Mining Algorithm ◽  
Trade Off ◽  
Fuzzy Association Rules ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Linguistic Term ◽  
Complete Set ◽  
Mining Algorithms

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values. Transactions with quantitative values are however commonly seen in real-world applications. We proposed a fuzzy mining algorithm by which each attribute used only the linguistic term with the maximum cardinality int he mining process. The number of items was thus the same as that of the original attributes, making the processing time reduced. The fuzzy association rules derived in this way are not complete. This paper thus modifies it and proposes a new fuzzy data-mining algorithm for extrating interesting knowledge from transactions stored as quantitative values. The proposed algorithm can derive a more complete set of rules but with more computation time than the method proposed. Trade-off thus exists between the computation time and the completeness of rules. Choosing an appropriate learning method thus depends on the requirement of the application domains.

Design and Implementation of Data Mining Based on Distributed Computing

2014 ◽  
Vol 644-650 ◽  
pp. 1702-1705 ◽  
Author(s):  
Jin Hai Zhang
Keyword(s):  
Data Mining ◽  
Distributed Computing ◽  
Data Storage ◽  
Leading Edge ◽  
Data Mining Algorithm ◽  
Dynamic Features ◽  
Computing Platform ◽  
Data Mining Algorithms ◽  
Rich Data ◽  
Mining Algorithms

Beacause internet data has a massive, diverse, heterogeneous, dynamic features, using traditional databases to analyze these data, data storage and processing efficiencies already can not meet the requirements. Utilizing leading-edge distributed computing technology to solve traditional data mining scenarios in lack of data mining of massive data improved data mining algorithm of lot OK Hadoop distributed computing platform, which later on other data mining algorithms using Hadoop to reference while using rich data mining algorithms can be found there is more value in your data.

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