apriori algorithm

The purpose of the present study was to Establishing an assessment recommender system to measure athletes' satisfaction with insurance services using Data Mining techniques. The method of this research is descriptive and is of survey type which is objective and applied in terms of collecting field information. The statistical population of the present study was athletes who regularly participated in sports in different sports in 2018, using Morgan and krejcie table 384 as a sample. The data collection tool was a researcher-made questionnaire whose validity was confirmed by 12 Sport management andinsurance specialists and its reliability was α = 0.86. The Kolmogorov-Smirnov test was used to normalize the data and Association Rules and apriori algorithm for classification and clustering of the algorithm. Data analysis was performed using SPSS version 23 and WEKA version 3.9.2. The findings showed that the most common injuries among athletes were upper limbs and dislocation. There is also a higher rate of injury in team disciplines. Results of satisfaction and ease of reimbursement of damages to insured athletes showed that athletes had moderate satisfaction with sports insurance services. as well as ensuring the most important component and accountability was the least important component in improving the satisfaction of athletes from insurance services. The results indicate that the number of athletes referring to the use of sports insurance plays an important role in the satisfaction of athletes with the services of the Sports Medical Federation.

In the current economy and entrepreneurship situation in developing countries, experts maintain that establishing startup companies proves an efficient and promising approach. In spite of the fact, many startup companies are failed. However, there are key factors, which can help such companies follow the path to success. This research aims to identify the factors, which led to success of startup companies in Iran. Accordingly, one of the important roles of data mining is exploring the relationship between datasets, and getting the result of a series of association-based rules for identifying the strong relations between business activities. The data of this study is collected from a total of 165 Iranian startup companies. The feature's columns included 41 success factors of startup companies addressed in domestic and foreign studies. For the prediction phase, we use the help of support vector machine algorithm, decision tree and k-nearest neighboring to classification. The feature selection technique in order to come up with the most efficient success factor of startup companies is Cuckoo search. Finally, rules are extracted by means of Apriori Algorithms. The results indicate that factors such as, namely, entrepreneurship experience, working duration, skills, type of service or product, target market, Blue Ocean or Red Ocean strategy, flexibility, scalability, customer loyalty, presence or lack of presence in an accelerator, and first-stage of investor are the most important factors that have the greatest effects in a startup's success. Moreover, the extracted results reveal that flexibility, and scalability are considered as two key factors contributing to success of Iranian startup companies.

There are many climatic factors affecting the number of patients in hospitals which generally tend to make a Non-optimal use of their facilities and human resources.This research is aimed at discovering hidden knowledge between climatic factors and the number of hospital patients using data mining techniques.

In this study, the relationship between climatic factors and the number of patients in Dr. Sheikh specialized pediatric hospital of Mashhad is investigated by classification based on multidimensional association rule mining. The number of patients in the nephrology, hematology, emergency and PICU department of this hospital have been considered separately, and consequently the relationship between the number of patients and the climatic factors such as air temperature, relative humidity, wind speed, air pressure and air pollution have been analyzed. This research has analyzed data gathered through a 19 month period and has been obtained by referring to the documents. In this research for feature selection, all subsets of climatic factors are searched and the effect of all subsets on the number of patients are evaluated using linear regression. Also for rule mining is used classification based on multidimensional association rule mining which is based on known Apriori algorithm.

The results show different patterns that indicate the relationship between the number of patients in the hospital departments with the climatic factors.

This study is able to help analyze the relationship between the climatic factors and the number of patients in the hospital. Also, the rules will help managers make optimal planning for hospital resources according to the different number of patients.

Increasing massive amount of data and using them to improve the quality of management decisions are the problems of libraries and information centers. In the meantime, a powerful data mining tool can analyze data to predict and analyze user transactions and stop wasting time to use useful data from among massive data, identify valid patterns and unknown relationships, and help libraries in strategic decision-making and effective management assistance. This study using association rules technique, which is one of the data mining methods, seeks to analyze the transactions of user lending and discover their access pattern. In this cross-sectional descriptive study required data were collected from transactions of users of the libraries and information centers of Hamedan University of Medical Sciences and after preprocessing these data, the total number of 6636 user transactions was 132833 using a census method during a three-year period examined. The results of this study showed that most transactions and referrals with the score of 20.7±24.4 were related to the students and the highest delay and duration of loan respectively with the score of 884.3±1396.9 and 885.7±1765.3 related to the faculty members. Also, the rules derived from the Apriori algorithm showed by providing a users access pattern based on demographic information and determining subject dependence of information resources could be used as an appropriate pattern for analyzing and predicting user transactions.  Using association rules technique and implementing the Apriori algorithm, the best rules governing the data set were extracted. Therefore, using association rules technique can be designed as a book advisory system in libraries and information centers. Managers and policy-makers can also use these patterns and rules to align their supply and information resources with the real needs of their members, and can benefit greatly from library procurement, collection, management and services.

In the current era, the study of the relationship between different markets and their impact on each other has become a necessity micro and macro investors. By investigating the relationship between markets, in addition to being able to obtain the required information about the impact of the markets, it can also help him in identifying various risks. According to the importance of the subject, in this paper, we have tried to investigate the relationship between the oil market and the gold, dollar markets, companies and energy funds in the field of energy, using the new associative rules approach and the Apriori algorithm. The use of associative rules explicitly explains the relationships between database fields and the relationships and interrelationships between a large set of data items. The results of this study indicate a direct relationship between the oil market and companies active in the energy sector, and the reverse relationship with the dollar index. There was also no significant relationship between the oil and gold market.

Apriori algorithm is the most popular algorithm in association rules mining. One of the problems the Apriori algorithm is that the user must specify a minimum support threshold. Consider that a user wants to implement the Apriori algorithm on a database with millions of transactions; Users will not have the necessary knowledge about all the transactions in the database and therefore cannot determine an appropriate threshold. The aim of this paper is improved the Apriori algorithm to automatically determine the minimum support. To achieve this, we will try to use fuzzy logic before of using the Apriori algorithm on data contained in the database, put the data in different clusters and try the offer to user the most appropriate threshold automatically. We hope this will be any rule that may be of interest not lost, because of inappropriate threshold specified by the user and also not extracted any rule useless.

Deregulation within the banking industry's and unprecedented growth competition in newtechnologies, every day the importance of keeping current customers and attract new customers are added. This study presents a two-step model to identify characteristics of different groups of bank customers, based on their profitability. The new criteria introduced to analyze the profitability of each customer. And then, this criterion has been used for clustering customers based on their profitability.Because in k-mean algorithm there is not a general rule for the optimal number of clusters and the number of clusters depend on the problem, therefore firstly with applied by Two-step clustering algorithm determine the optimal number of clusters. In this study, Customers clustering to 3 groups as golden, silver, and bronze customers. Then, by using K-mean algorithm different groups of customers are identified. And with help of Apriori algorithm association rules of each cluster is inference. The result of this exploration, help to banks have better understanding of current and future customer expectations. And through this may be facilitated develop of marketing strategies to attract and retain profitable customers.