scholarly journals PO-088 Effect of Aerobic Exercise on Myocardial Mitochondria Fusion-Fission in Chronic Intermittent Hypoxia Mice

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Yulong Hu ◽  
Juan Chen
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Association Rules ◽  
Association Rule ◽  
Competitive Sports ◽  
Analysis Method ◽  
Mining Technology ◽  
Scientific Training ◽  
Biochemical Indicators ◽  
Analysis And Evaluation

Objective Training monitoring is an important part of scientific training, and also accumulated a large amount of data, but the analysis and evaluation of biochemical indicators are mostly concentrated on the level of experience and the general, phased and individualized research application of statistical methods. The data mining technology is applied to the analysis and evaluation of the biochemical indexes of competitive sports, the analysis of the data is carried out in the deep level, the potential, new and useful information and knowledge are extracted, and the new exploration ideas are carried out for the analysis of the biochemical indexes of competitive sports, and a more reliable and more powerful data branch is provided for the scientific and efficient training support. Methods Using the literature data method, logic analysis method and expert interview method, the application of the current data mining technology in the analysis of biochemical indicators is summarized. Results The scientific analysis and evaluation of athletes' physical function status has been the focus of domestic and foreign coaches and sports researchers. The application of data mining technology in sports biochemical indicators is also becoming more and more extensive. For example, Mao Jie and others applied the gray ART clustering model analysis method to the monitoring of competitive sports biochemical indicators. Through this data mining model, the coach can easily judge the athlete's competitive physical condition, and can provide a scientific basis for correct training according to the different competitive conditions of each athlete, using different training guidance programs and training methods. Ma Jing et al. explored the feasibility of applying decision tree algorithm and association rules in volleyball biochemical analysis. It was found that C5.0 decision tree and Apriori association rule algorithm can be used to predict and analyze the technical level of women's volleyball players. Li Guangjun and others successfully applied the association rule data mining to the biochemical data analysis of canoeists, and provided a basis for scientific decision-making and analysis of sports training and athlete selection. Zhang Hui designed a data mining system for sports biochemical index based on association rules. The results show that the system has fast data mining rate, short time consuming and high reliability. It provides a more scientific evaluation standard for the data mining of sports biochemical index, and also provides a basis for the future training program. Conclusions With the development of competitive sports, in order to achieve new heights, the application of data mining technology to vast biochemical data is of great significance for the establishment of scientific training evaluation methods and standards, and is also the inevitable development of future sports scientific research.

Study on Application of Data Mining Technology in Syndrome Differentiation of TCM

2013 ◽  
Vol 765-767 ◽  
pp. 282-285
Author(s):  
Zhi Guo Dai ◽  
Yang Yang Han
Keyword(s):  
Data Mining ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Syndrome Differentiation ◽  
Rule Mining ◽  
Mining Technology ◽  
Disease Symptoms

Study on the applications of association rule mining in traditional Chinese medicine (TCM) knowledge and experience is carried out in this paper. The association rules of disease symptoms and syndrome differentiation, syndrome differentiation and prescription, disease symptoms and prescription are mined by analyzing the cases of patients with chronic gastritis, and then the mined association rules are interpreted that provide the beneficial reference for data mining technology in TCM.

Hiding Sensitive Association Rules by Sanitizing

2013 ◽  
Vol 694-697 ◽  
pp. 2317-2321
Author(s):  
Hui Wang
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Mining Technology ◽  
Data Mining Techniques ◽  
Confidential Data ◽  
Knowledge Hiding ◽  
Existing Data

The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.

scholarly journals ASSOCIATION RULE DALAM MENENTUKAN CROSS-SELLING PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH

2020 ◽  
Vol 7 (4) ◽  
pp. 102
Author(s):  
Delila Melati ◽  
Titi Sri Wahyuni
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Analysis Method ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Time Processing ◽  
Processing Data ◽  
Analyze Data

Sales transaction data at Bigmart stored in a database will be able to become new knowledge if processed using the data mining process. In addition, inventory is also a problem that is being faced by Bigmart. Data mining is able to analyze data into information in the form of transaction patterns that are useful in increasing revenue, one of which is Cross-Selling products. Association rule is one of the data mining methods included in the Market Basket Analysis method. The algorithm used is the FP-Growth algorithm because it has the virtue of shorter time processing data. The pattern obtained is determined by the value of support (support) and the value of confidence (confidence). To find the association rules the FP-Growth algorithm is used. To get more accurate association rules, use the Weka 8.3 tool. There are 11 association rules obtained using the Weka 8.3 tool which is classified as a Stong Rule that meets the Minimum support value of 10% and Minimum confidence 80%. Keywords: Database, Cross-selling, Market Basket Analysis, Association Rule, FP-Growth

scholarly journals Analyze and Enhance Sales in Lulu Supermarket using Data Mining Technology

2020 ◽  
Author(s):  
Ahmed Abdullah Awadh Koofan ◽  
Mohammed Kaleem
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Association Rules ◽  
Association Rule ◽  
Relevant Information ◽  
Research Association ◽  
Mining Technology ◽  
Time Consumption ◽  
Huge Data ◽  
Using Data

-Data mining is a powerful technology for analyzing huge data, it has many techniques such as; classification, clustering, prediction and association rules etc., In this research Association rule will be used for analyzing data, which will help to extract the data related to combinations of items. Numerous customers tends to purchase items regularly, each time they visit supermarket, customer’s need to move around from shelf to shelf for the product of their interest which is time consuming. This research will help to minimize the time consumption for customers by analyzing the customer’s invoices and letting know the supermarket about the patterns of customer's orientations. In this work python tool will be used for data mining, by using association rule to analyze the customer’s purchases and retrieve the relevant information which will help to determine the customer’s pattern and know the association between products. In this rationale, the data of customer’s purchases were collected from Lulu hypermarket for data analysis and the outcomes of the analysis is to know the customer’s patterns and making the shopping easy by reorganizing the related items and the most buying items together on same shelf.

The Searching of Decision Tree Based on Data Mining

2013 ◽  
Vol 380-384 ◽  
pp. 2633-2636 ◽  
Author(s):  
Yu Ling Ma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Information Technology ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Set Theory ◽  
Association Rules ◽  
Mining Technology ◽  
Artificial Neural

With the promotion of information technology in various fields, the amount of data grow crazily, how to find what we need in the information ocean is a problem to be solved, and the data mining technology has made the issue to be resolved. This paper introduced the data mining knowledge briefly, for example: set theory, decision tree, clustering and association rules, artificial neural network, genetic algorithm and so on, then analysis the method of decision tree in detail by example.

Predicting customer churn in mobile industry using data mining technology

2017 ◽  
Vol 117 (1) ◽  
pp. 90-109 ◽  
Author(s):  
Eui-Bang Lee ◽  
Jinwha Kim ◽  
Sang-Gun Lee
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Online News ◽  
Partial Least Square ◽  
Churn Prediction ◽  
Mining Technology ◽  
Content Type ◽  
Customer Churn ◽  
Mobile Industry ◽  
Using Data

Purpose The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data. Design/methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model. Findings This study found prediction rates similar to those delivered by subscriber data-based analyses. In addition, because previous studies do not clearly suggest the effects of the factors, this study uses decision tree graphing and PLS modeling to identify which words deliver positive or negative influences. Originality/value These findings imply an expansion of churn prediction, advertising effect, and various psychological studies. It also proposes concrete ideas to advance the competitive advantage of companies, which not only helps corporate development, but also improves industry-wide efficiency.

Application of data mining technology and wireless network sensing technology in sports training index analysis

2020 ◽  
Author(s):  
Liqiu Qian ◽  
Jiatong Liu
Keyword(s):  
Data Mining ◽  
Test Accuracy ◽  
Analysis Method ◽  
Sports Training ◽  
Analysis Model ◽  
Data Types ◽  
Mining Technology ◽  
Accuracy Test ◽  
Index Analysis ◽  
Sensing Technology

Abstract The conventional analysis method can provide a general analysis of sports training index, but its ability is relatively low when analyzing niche data. To solve this problem, this paper proposes data mining technology. First, the indicator parameter classification is determined, then the data mining technology is imported, the sports training analysis mechanism is established through this technology, and the construction of the index analysis model is completed. The model is used to analyze the process of niche data mining, and effective data of training indicators are obtained. Deep learning is a method of machine learning based on representation of data.Through the coverage test, accuracy test and immunity test, the variable parameters of the comprehensive analysis capability are determined. Further calculation of this parameter shows that the comprehensive ability of the data mining application analysis method is improved by 37.14% compared with the conventional method, which is suitable for analysis of niche sports training indicators of different data types.

XML-Enabled Association Analysis

2011 ◽  
pp. 2117-2122
Author(s):  
Ling Feng
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Xml Data ◽  
Market Basket ◽  
Transactional Databases ◽  
Mining Association Rules

The discovery of association rules from large amounts of structured or semi-structured data is an important data mining problem [Agrawal et al. 1993, Agrawal and Srikant 1994, Miyahara et al. 2001, Termier et al. 2002, Braga et al. 2002, Cong et al. 2002, Braga et al. 2003, Xiao et al. 2003, Maruyama and Uehara 2000, Wang and Liu 2000]. It has crucial applications in decision support and marketing strategy. The most prototypical application of association rules is market basket analysis using transaction databases from supermarkets. These databases contain sales transaction records, each of which details items bought by a customer in the transaction. Mining association rules is the process of discovering knowledge such as “80% of customers who bought diapers also bought beer, and 35% of customers bought both diapers and beer”, which can be expressed as “diaper ? beer” (35%, 80%), where 80% is the confidence level of the rule, and 35% is the support level of the rule indicating how frequently the customers bought both diapers and beer. In general, an association rule takes the form X ? Y (s, c), where X and Y are sets of items, and s and c are support and confidence, respectively. In the XML Era, mining association rules is confronted with more challenges than in the traditional well-structured world due to the inherent flexibilities of XML in both structure and semantics [Feng and Dillon 2005]. First, XML data has a more complex hierarchical structure than a database record. Second, elements in XML data have contextual positions, which thus carry the order notion. Third, XML data appears to be much bigger than traditional data. To address these challenges, the classic association rule mining framework originating with transactional databases needs to be re-examined.

Constraint-Based Association Rule Mining

2011 ◽  
pp. 307-312 ◽  
Author(s):  
Carson Kai-Sang Leung
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Computational Cost ◽  
Knowledge Discovery In Databases ◽  
Rule Mining ◽  
The Subject ◽  
User Focus ◽  
High Computational Cost

The problem of association rule mining was introduced in 1993 (Agrawal et al., 1993). Since then, it has been the subject of numerous studies. Most of these studies focused on either performance issues or functionality issues. The former considered how to compute association rules efficiently, whereas the latter considered what kinds of rules to compute. Examples of the former include the Apriori-based mining framework (Agrawal & Srikant, 1994), its performance enhancements (Park et al., 1997; Leung et al., 2002), and the tree-based mining framework (Han et al., 2000); examples of the latter include extensions of the initial notion of association rules to other rules such as dependence rules (Silverstein et al., 1998) and ratio rules (Korn et al., 1998). In general, most of these studies basically considered the data mining exercise in isolation. They did not explore how data mining can interact with the human user, which is a key component in the broader picture of knowledge discovery in databases. Hence, they provided little or no support for user focus. Consequently, the user usually needs to wait for a long period of time to get numerous association rules, out of which only a small fraction may be interesting to the user. In other words, the user often incurs a high computational cost that is disproportionate to what he wants to get. This calls for constraint-based association rule mining.

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