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.

An Efficient Genetic Simulated Annealing Association Rules Method

2010 ◽  
Vol 34-35 ◽  
pp. 927-931
Author(s):  
Jun Jie Cen ◽  
Guo Hong Gao ◽  
Ying Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Data Analysis ◽  
Association Rules ◽  
Association Rule ◽  
Web Site ◽  
Web Mining ◽  
Incremental Mining ◽  
Mining Technology

Association rule is one of the important models of Web mining. By analyzing the topology of web site, this paper brings forward an efficient genetic simulated annealing association rules method．It applies genetic algorithm，incremental mining technology to trace users access behavior and optimizes association rules，and forecast capable association rules which improves its precision．Finally, this paper gives out the data analysis of experiment and summarizes the characteristics of genetic mining．

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.

Application of Data Mining in Data Analysis of Tobacco Consumption

2011 ◽  
Vol 282-283 ◽  
pp. 770-773
Author(s):  
Rong Liang Luo
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Product Design ◽  
Association Rules ◽  
Tobacco Consumption ◽  
Apriori Algorithm ◽  
Tobacco Company ◽  
Mining Technology ◽  
Design Improvement

Development of data mining technology provides convenience for analyzing tobacco consumers’ act. Through simple introduction on contents and categories of data mining technology, the survey on tobacco consumption act of Shaoxin Tobacco Company is analyzed with association rules and data mining software Weka, and factors which affect tobacco consumption are mined on with association with Apriori Algorithm, so as to provide valuable references for brand spreading channels, product design, improvement of taste and flavor, package, price and other aspects for the tobacco company.

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.

The Application of Data Mining Technology in the Data Analysis of Traditional Chinese Medicine

2014 ◽  
Vol 687-691 ◽  
pp. 1266-1269
Author(s):  
Zhen Wang ◽  
Kan Kan She
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Analysis ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Rapid Development ◽  
Theoretical Knowledge ◽  
Mining Technology ◽  
Mining Methods ◽  
Using Data

With the rapid development of information technology, the amount of data accumulated by people is increasing sharply. Data mining technology is an effective method to find useful information from vast amounts of data and increase the utilization of information. After thousands of years of development, traditional Chinese medicine has accumulated a wealth of theoretical knowledge and a lot of books and records, more and more Chinese medicine databases are created. Using data mining technology to mine the unknown knowledge and rules and put forward assumptions for experiment and theory can be a good auxiliary research of traditional Chinese medicine. This article analyzes the data mining methods of traditional Chinese medicine at first. Then, the application of data mining technology in traditional Chinese medicine data analysis is introduced which includes the data mining of traditional Chinese medicine literatures, diagnosis and clinic of traditional Chinese medicine and prescription and medication of traditional Chinese medicine. At last, the aspects which need to be paid attention to in the data mining of traditional Chinese medicine are pointed out.

scholarly journals Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods

2018 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Taqwa Hariguna ◽  
Uswatun Hasanah ◽  
Nindi Nofi Susanti
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Processing Data ◽  
Using Data

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer

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.

scholarly journals Classification of Pruning Methodologies for Model Development using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 2043-2047 ◽  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Model Development ◽  
Data Mining Techniques ◽  
Rule Set ◽  
Using Data ◽  
Rule Pruning ◽  
Class Association Rule

Knowledge discovery process deals with two essential data mining techniques, association and classification. Classification produces a set of large number of associative classification rules for a given observation. Pruning removes unnecessary class association rules without losing classification accuracy. These processes are very significant but at the same time very challenging. The experimental results and limitations of existing class association rules mining techniques have shown that there is a requirement to consider more pruning parameters so that the size of classifier can be further optimized. Here through this paper we are presenting a survey various strategies for class association rule pruning and study their effects that enables us to extract efficient compact and high confidence class association rule set and we have also proposed a pruning methodology..

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