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iCatse International Conference on Information Science and Applications (ICISA)

作者： Huh, Jun-Ho Kim, Han-Byul Kim, Jinmo Catholic Univ Pusan Dept Software Busan South Korea Pukyong Natl Univ Dept Comp Engn Busan South Korea

ISBN: (纸本)9789811041549;9789811041532

The Republic of Korea (ROK) has emerged as a country of superior medical tourism in the last decade among the people of China, Japan, Southeast Asia, Russia and the Middle East for the plastic surgery or others requiring precision skills. Although the ROK's medical tourism industry grew quantitatively in its revenue and the number of visitors, the report from the 2015 World Economic Forum concerning the competitiveness of ROK's tourism including the medical tourism showed that its rank had dropped to 29th position, a drop of 4 places from 25th in 2013. Thus, it is about time to improve the situation by investigating the actual conditions of tours taken by the foreign tourists to establish new strategy, which is the main contribution of this research. As a research method, a big data analysis has been performed on the basis of machine learning and using R-studio. During the analysis process, there were some relevant regularities which were difficult to be found in the big data and based on these findings, we have attempted to find the solutions for the bad images that foreign visitors had shared in common. The result of the big data analysis showed that the their purpose of visit was different from each other depending on the age groups and the details of their experience of inconvenience varied as well.

关键词： Big data R Machine learning Medical tourism apriori algorithm

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Privacy Preserving Data Mining Using Association Rule Based on apriori algorithm 1st

Privacy Preserving Data Mining Using Association Rule Based ...

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1st International Conference on Advanced Informatics for Computing Research (ICAICR)

作者： Rehman, Shabnum Sharma, Anil Lovely Profess Univ Phagwara India

ISBN: (纸本)9789811057809;9789811057793

Data mining is a process of extracting knowledge from the large databases. This has made data mining a significant and functional emerging trend. Association rule is one of the most used data mining techniques that discover hidden correlations from huge data sets. There are several mining algorithms for association rules apriori is one of the most popular algorithm used for extracting frequent item sets from databases and getting the association rule for knowledge discovery. The time required for generating frequent item sets plays an important role. Based on this algorithm we are performing comparison of sanitized data and existing data based on number of iterations and the execution time. The experimental results shows that the number of iteration is reduced in sanitized data than that of existing data also the time is reduced in sanitized data. The association rule generation leads to ensure privacy of the dataset by creating items so, in this way privacy of association rules along with data quality is well maintained.

关键词： Data mining Data privacy Association rule mining apriori algorithm

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Quantitative Analysis of Frequent Itemsets Using apriori algorithm on Apache Spark Framework 3rd

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3rd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Dharavath, Ramesh Raj, Shashi Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

ISBN: (纸本)9789811038747;9789811038730

Frequent itemset mining is one of the popular techniques used to discover hidden knowledge from large-scale transactional datasets in a wide range of applications. apriori algorithm is considered as a typical algorithm to find frequent itemsets in market basket analysis. Since its inception, many efforts have been made to enhance the efficiency of the original algorithm. MapReduce model is one of the efficient tools to implement parallel and distributed computing, so that large-scale data set algorithms such as apriori algorithm can be made efficient in terms of speed up and other related parameters. One of the major drawbacks of the MapReduce model is that it is not suitable for iterative jobs/tasks due to overheads imposed. Now-a-days, Apache Spark is getting huge attention for iterative jobs because of its in-memory processing capabilities. Most of the frequent pattern mining algorithms consider only distinct items in a transaction. For transactional data analysis, multiple occurrences of an item or in other words "quantities" by which a particular item is purchased in the same transaction can be important to derive additional information about frequent itemsets. In this script, we propose a modified version of the apriori algorithm based on Apache Spark framework that not only mines the frequent itemsets in the input transactional data but also analyzes related quantities of the items for a particular itemset to find the most frequent quantity being purchased for every frequent itemset. Experiments are conducted to gain insight in the form of effectiveness, efficiency, and scalability of the proposed approach.

关键词： apriori algorithm Big data Apache spark

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Improved apriori algorithm Using Power Set on Hadoop 1st

Improved Apriori Algorithm Using Power Set on Hadoop

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1st International Conference on Computational Intelligence and Informatics (ICCII)

作者： Imran, Abdullah Ranjan, Prabhat Cent Univ South Bihar Patna Bihar India

ISBN: (纸本)9789811024719;9789811024702

In this era of big data, a huge volume of data is produced. Storage and analysis of such data is not possible by traditional techniques. In this paper, a good method to implement the MapReduce apriori algorithm using vertical layout of database along with power set and concept of Set Theory of Intersection have been proposed. The vertical layout has the advantage of scanning only a limited number of records for calculating the support of an item. By use of the power set concept, we are able to generate frequent item set, in just two scans of database, reducing the complexity. The concept of set intersection is used to calculate its support. The result shows good improvement over the existing MapReduce apriori algorithm.

关键词： apriori algorithm Association rule Big data Frequent item set Hadoop

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Research and Optimization of apriori algorithm Based on Cloud Computing and Medical Large Data

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AGRO FOOD INDUSTRY HI-TECH 2017年第1期28卷 697-700页

作者： Song, Menghua Tianjin Maritime Coll Tianjin Peoples R China

With the rapid development of the Internet, the variety and quantity of various medical data resources have experienced large-scale growth. The algorithmic organization structure of fragmented medical data resources can accelerate the standardization of local resources and improve resource utilization rate. Thus, the optimization of apriori algorithm based on cloud computing and medical data was studied. This paper introduced an overview of cloud computing and medical data and described apriori algorithm based on the cloud computing and medical data optimization process. Results show that the application of apriori algorithm based on cloud computing and medical data was realistic and reasonable.

关键词： Cloud computing medical large data apriori algorithm algorithm optimization

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Power Big Data apriori Parallel algorithm Based on Posterior Double Threshold

Power Big Data Apriori Parallel Algorithm Based on Posterior...

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2nd IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Liu Fei Zhang Yiying Pang Haoyuan Wang Sining Li Yun Sun Lei Tianjin Univ Sci & Technol Coll Comp Sci & Informat Engn Tianjin 300457 Peoples R China Beijing GuoDianTong Network Technol Co Ltd Beijing 100070 Peoples R China

ISBN: (纸本)9781538685495

Aiming at the problem of low computational efficiency of traditional power data, this paper proposes a posteriori double-threshold power big data apriori parallel computing method based on Boolean matrix. The Boolean matrix is used to compress and store data, and the removal rate is introduced to pruning the data. The double-threshold parallel computing method is used, and then the lifting degree is used to verify it. The Boolean matrix-based posterior dual-threshold power big data apriori parallel computing method is used to solve the problem of low efficiency of traditional power big data calculation. The experimental results show that the improved algorithm can effectively improve the computational power and computational efficiency of power big data.

关键词： Boolean matrix Double threshold Power big data apriori algorithm

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Research on library personalized service based on apriori algorithm

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AGRO FOOD INDUSTRY HI-TECH 2017年第1期28卷 2555-2559页

作者： Zhang, Cuiyuan Mudanjiang Normal Univ Mudanjiang Heilongjiang Peoples R China

With the development of digital network information technology, it is very important to use data mining technology to mine useful information. The library personalized service can make the library work more effectively by using data mining technology. apriori algorithm is a classic algorithm in data mining technology. In this paper, the library personalized service was researched by using the apriori algorithm. The concept and basic principle of data mining technology and association rules were mainly elaborated, and a classical algorithm, the apriori algorithm in association rule was analyzed. Based on the traditional algorithm, the apriori algorithm was improved, the new algorithm H_apriori algorithm was obtained, and the new algorithm was compared with the apriori algorithm. A library personalized service system was constructed based on the improved algorithm, which is based on the information of the library database. The experimental evaluation was carried out on the constructed system.

关键词： Data mining association rule apriori algorithm personalized service

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Application of a two-step cluster analysis and the apriori algorithm to classify the deformation states of two typical colluvial landslides in the Three Gorges, China

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ENVIRONMENTAL EARTH SCIENCES 2016年第2期75卷 1-16页

作者： Wu, Xueling Zhan, F. Benjamin Zhang, Kaixiang Deng, Qinglu China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China Minist Land & Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518034 Peoples R China Texas State Univ Texas Ctr Geog Informat Sci Dept Geog San Marcos TX 78666 USA China Univ Geosci Sch Engn Wuhan 430074 Peoples R China

Several extensive landslides have occurred in the vicinity of the Three Gorges Reservoir since its initial impoundment in June 2003. A reduction of the landslide risk is essential for the safety and security of lives and property in the region. This study analyses the deformation states of two typical colluvial landslides (the Baijiabao and Laoshewo landslides) using 6 years of monitoring data, a two-step cluster analysis, and the apriori algorithm. The landslide displacement versus time curves exhibit step-like patterns, and the landslide deformation is highly correlated with fluctuations in the reservoir level and seasonal precipitation. To determine different types of landslide deformation, the monthly displacement curves of the colluvial landslides are classified into three types using a two-step cluster analysis: initial deformation, constant deformation, and rapid deformation. Five driving factors were selected as the antecedents for the apriori algorithm to obtain rules that describe the relationships between the landslide deformation and the influential parameters. These factors include the cumulative precipitation over the previous month, the maximum daily precipitation during the current month, changes in the reservoir level over the previous month, cumulative increases in the reservoir level and the average reservoir level during the current month. The analytical results were validated by comparing them with observed landslide deformation characteristics using three measurement standards: support, confidence and lift. The results show that the combined method of a two-step cluster analysis with the apriori algorithm can effectively model the landslide deformation states that are associated with the Baijiabao and Laoshewo landslides. Moreover, this method may serve as a potential reference for deformation analyses of colluvial landslides in the Three Gorges.

关键词： Landslides Deformation apriori algorithm Two-step cluster Three Gorges Reservoir

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Discussion and Improvement of apriori algorithm of Data Mining Based on Hadoop Platform

Discussion and Improvement of Apriori Algorithm of Data Mini...

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2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology(FMSMT 2017)

作者： Mengyang Zhao Bo Tang Le Yang Electronics and Information Engineering Institute of Sichuan University

ISBN: (纸本)9781510840232

apriori algorithm can accurately find out the related items in the database, and it can be used in various fields of work and life. However, with the explosive growth of data, there are still some disadvantages in the practical application of the algorithm. This paper introduces the Hadoop platform, explores the characteristics of apriori algorithm, and puts forward a kind of improved apriori algorithm based on the Hadoop platform to provide some references for the relative researchers.

关键词： apriori algorithm data mining Hadoop platform

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Analysis of HSV-1 and HSV-2 that Cause Herpes Simplex with apriori algorithm, Decision Tree, and Support Vector Machine

Analysis of HSV-1 and HSV-2 that Cause Herpes Simplex with A...

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International Conference on Advanced Communication Technology

作者： Juhyun Lee Yeojin Jung Seungho Shin Taeseon Yoon International course HAFS (Hankuk Academy of Foreign Studies) Faculty

ISBN: (纸本)9781509048922

The Herpes Simplex is a common fluid-intermitted disease that is common among not only adults but also children. Several symptoms include blisters and sores forming on infection sites that are mainly located in oral and genital body parts. The two main types of viral infection, commonly referred to as HSV-1 and HSV-2, are both part of the same Herpes Simplex Virus group (HSV), but they exhibit different characteristics in the main target of infection and the location of the infection sites. Normal cases of HSV-1 cause oral lesions and has less dangers of cross-generation infection when the female is pregnant, whereas HSV-2 cause genital lesions and usually is co-infected with human immunodeficiency virus type 1(HIV-1). Currently, no specific permanent vaccination is available for this disease and since it is chronic and can be transmitted as long as symptoms are present, the development of vaccination is significant at this stage. In order to provide further possibilities for vaccination development, we analyzed the genomes of HSV-1 and HSV-2 by using apriori algorithm, Decision tree algorithm, and Support Vector Machine. Thus, we extracted results that showed the similarity and differences of the two genomes, mainly focusing on the prominent distribution of amino acids in each of the 3 categories we have chosen - dividing the genomes up in to 13, 17, and 19 windows. Through this, we have showed further potential in developing effective vaccination more specific to each type of Herpes Simplex.

关键词： Herpes Simplex HSV-1(herpes simplex virus type 1) HSV-2(herpes simplex virus type 2) apriori algorithm Decision Tree SVM (Support Vector Machine)

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