The Exploration of Data Mining in Face of Cloud Computing

2013 ◽  
Vol 664 ◽  
pp. 1066-1071
Author(s):  
Chen Wang ◽  
Shu Xiang Li
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Massive Data ◽  
Data Mining Algorithms ◽  
Mining Algorithms

This paper first discusses the limitations and shortcomings of data mining under the conditions of massive data. Combined with the advantages of cloud computing, a data mining architecture for cloud computing is designed, and on this basis, the paper discusses the improvement of data mining algorithms for cloud computing. As a theoretical exploration, the paper proposed useful suggestions for the optimization of data mining in face of cloud computing.

The Development of Data Mining System Based on Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 2280-2283
Author(s):  
Qiong Ren
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Current Data ◽  
Mining System ◽  
Computing Platform ◽  
Data Mining Algorithms ◽  
Cloud Computing Platform ◽  
Data Mining System ◽  
Process Cost ◽  
Mining Algorithms

With the increasing of input data size, process cost will be very long, for the explosive growth of the Internet data even reached the point of single machine can handle. This article mainly introduces the architecture of the concept of cloud computing and, the mainstream of the analysis of the current data mining algorithms, based on cloud computing to develop the data mining system, providing the operation feasibility of data mining in cloud computing platform, having strong guiding significance.

Environmental Examination of Data Mining Algorithms Based on Cloud-Computing

2013 ◽  
Vol 380-384 ◽  
pp. 2911-2914
Author(s):  
Yi Zhuo Guo ◽  
Tao Dai
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Cluster Computing ◽  
Programming Model ◽  
Test System ◽  
Parallel Execution ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Parallel Programming Model ◽  
Mining Algorithms

This article on cloud computing and data mining to a more comprehensive study to introduce the concept of cloud computing and data mining, pointed out that the traditional data mining techniques in the case of network test system of massive data mining, processing speed is slow, the load is not balancing and node efficiency is not high enough, Apriori algorithm based on the Map/Reduce parallel programming model, the distributed nature of cloud computing environments, make full use of cluster computing resources to support the parallel execution of algorithms by examples of cloud computing after Apriori algorithm in cloud computing environment to get higher efficiency of frequent itemsets mining algorithm performance than traditional data mining.

scholarly journals DATA MINING ALGORITHM BASED ON CLOUD COMPUTING

2018 ◽  
Vol 48 (4) ◽  
pp. 281-285
Author(s):  
Y. J. HAO
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Cellular Neural Network ◽  
Data Mining Algorithm ◽  
Computing Technology ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Hash Algorithm ◽  
Mining Algorithms ◽  
Big Data Technology

The data mining algorithm based on cloud computing is studied and analyzed in this paper. Firstly, the research status and background of the data mining algorithms based on cloud computing are introduced briefly. Secondly, the design of Hash algorithm under cellular neural network is introduced which is needed in this paper. Next, the design of wavelet data compression algorithm for wireless sensor networks is described. Finally, the experimental results and the optimization similarity analysis are obtained. The analysis results show that the data mining algorithm based on cloud computing constructed in this paper plays an important role in data mining, and can improve the data mining algorithm of cloud computing and the development level of cloud computing technology and big data technology to some extent.

scholarly journals An In-Depth Analysis of The Data Mining Algorithms and Their Effective Applicability in Health and Biomedical Informatics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Deeya Tangri
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Medical Science ◽  
Massive Data ◽  
Medical Field ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Depth Analysis ◽  
Data Extract ◽  
Mining Algorithms

Nowadays, the Health care industry is one of the fastest-growing industries. As we already know, health care has researched very widely, introducing many medical data that is not easy to mine. Data mining is an approach that helps to discover essential data from massive data or collection of data. So, in medical Science, there is a need for tools that help analyses the data, extract the significant result from massive data, and discover efficient use of information. Generally, three things are mandatory in medical for every patient. First is patient details, diagnosis and medications. Converting these data into a basic pattern for predicting the patient disease helps in early diagnosis. This research mainly focuses on the data mining approach, which is widely considered in the medical field.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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