Papers by Keyword: Apriori Algorithm

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Authors: Si Hui Shu, Zi Zhi Lin
Abstract: Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.
Authors: Hai Peng Ji, Tai Yong Wang, Jing Liu, Shi Yan Fan, Zhi Peng Wang, Kai Ran Zhang
Abstract: With the development of Internet industry, equipment data is increasing. The traditional method is not suitable for processing large data. Aiming at inefficient problem of Apriori algorithm when mining very large database, an efficient parallel association rules mining algorithm (Advanced Pruning Parallel Apriori Algorithm) based on a cluster is presented. APPAA algorithm can enhance the mining efficiency, as well as the system’s extension. Experimental results show that APPAA algorithm cuts down 85% mining time of Apriori, and it has good characteristics of parallel and it is suitable for mining very large size database of fault diagnosis.
Authors: Na Xin Peng
Abstract: Aiming at the problem that most of weighted association rules algorithm have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, Boolean weighted association rules algorithm and weighted fuzzy association rules algorithm are presented, which use pruning strategy of Apriori algorithm so as to improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.
Authors: Yu Han Zhou, Pei Tian
Abstract: With the explosive growth of medical data, the new method of data mining is applied now. Data mining has provided knowledge, rules and decision for the whole medical database or the integration of medical information. This paper uses an improved Apriori algorithm to analyze the characteristics of medical data and find knowledge in medical data. This algorithm improves the efficiency and reduces the hardware requirement.
Authors: Jin Shan Huang
Abstract: The main purpose of computer network security research is extract system data and factors related to the computer network security, and then establish a model which is analyze the security of computer network. Analyze the association rules and its related mining algorithm Apriori, through the application of the improved Apriori algorithm founded on pretreatment in computer network security teaching; explain the process of data mining and analysis of mining results, finally points out the future research direction. This model mainly analyzes the contents of three aspects as the requirements of computer network system security, degree of safety and threats to computer security, to check the security of the computer performance.
Authors: Yi Qian Cang, Xiao Feng Zhou, Fa Chao Zhou, Ai Hua Gu, Tian Liu, Zhen Cao
Abstract: With the informatization development of water conservancy, the data of water conservancy increased gradually. The data of river regime comes up with the features of real-time and historicity. Therefore, the data has the character of diversity, large and abundant contents. Because of the restrict of the classic Apriori algorithm, many researchers cant meet the requirements of those large data analysis. So, this thesis explained the improvements of the classic Apriori algorithm, designed and elaborated the effectiveness of association rules in river regime resource.
Authors: Wei Peng Zhang
Abstract: To apply Apriori algorithm to analysis on association of stroke and hemorheology, and obtain the meaningful medical information. A large number of hemorheology data of patients with stroke were collected, including whole blood viscosity low cut, whole blood viscosity medium cut, whole blood viscosity high cut, blood sedimentation, hematocrit, plasma viscosity, thrombus, age, sex. Minimum support was 0.2 and minimum confidence was 0.8 as experience for analysis of association rules with apriori algorithm. Four strong association rules were screened by the objective and subjective interestingness, which contained the relation between the stroke and age, sex, whole blood viscosity, plasma viscosity. The results show that Apriori algorithm can be used to study the the diagnosis and prevention of stroke.
Authors: Lei Wang, Cun Xiao Yi
Abstract: How to improve the employment rate of graduates is an important task for higher vocational colleges to solve. In order to effectively improve their Employment competitiveness, advice should be made to help students to enhance specific kinds of learning and ability. Association Rules Mining is one core of the Data Mining Association Rules, it’s helpful in finding useful information hidden in complex data. By using Association Rules Mining in finding the knowledge and ability which helps employed students to earn their jobs, necessary ability for each kind of job can be found, and then advice offered for students to target their employment career will be more exact and proper.
Authors: Jian Xing Chen
Abstract: With the continuous expansion of computer simulation scale, the demand for data mining algorithm is also more and more big. The difficulties in computer data mining technology are focused on algorithm development. Apriori algorithm is a kind of computer data mining algorithm which can greatly improve the computational efficiency. The algorithm uses association rule, which can avoid repeated frequently by layer scanning, reducing the computer time. This paper uses Apriori algorithm to design the data mining parameter optimization model of computer 3D human biology simulation, and applies to improve the step three jump. Through the simulation we found step distance appropriate, it provides technical reference for the application of computer simulation technology in sports.
Authors: Li Gang Liu, Chen Fu Yi
Abstract: Along with the development of artificial intelligent and computer science, data mining has become a powerful analytical tool to obtain the interested and important information from the amount of noisy and fuzzy data. In this paper, data mining technology is applied to the charging system in hospital. Usually, many unnecessary medical requirements are added to the treatment of patients, which could result in the extra pay and time for such treatment. Thus, this paper presents the definition of one-fee system in hospital for the common disease cases by applying data mining technology, in which, artificial neural networks and association rules (e.g., apriori algorithm) are presented. The presented one-fee definition might provide a reference for the charging system in hospital.
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