Application of Data Mining Technology Based on Apriori Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2036-2039
Author(s):  
Jian Xing Chen
Keyword(s):  
Data Mining ◽  
Computer Simulation ◽  
Association Rule ◽  
Computer Time ◽  
Data Mining Algorithm ◽  
Apriori Algorithm ◽  
Mining Technology ◽  
Computer Data ◽  
Mining Algorithm ◽  
Step Distance

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.

Research on Association Rule Mining Algorithm Based on Distributed Data

2014 ◽  
Vol 998-999 ◽  
pp. 899-902 ◽  
Author(s):  
Cheng Luo ◽  
Ying Chen
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Frequent Itemsets ◽  
Network Communication ◽  
Data Mining Algorithm ◽  
Distributed Data ◽  
Rule Mining ◽  
Mining Algorithm

Existing data miming algorithms have mostly implemented data mining under centralized environment, but the large-scale database exists in the distributed form. According to the existing problem of the distributed data mining algorithm FDM and its improved algorithms, which exist the problem that the frequent itemsets are lost and network communication cost too much. This paper proposes a association rule mining algorithm based on distributed data (ARADD). The mapping marks the array mechanism is included in the ARADD algorithm, which can not only keep the integrity of the frequent itemsets, but also reduces the cost of network communication. The efficiency of algorithm is proved in the experiment.

scholarly journals Human Resource Allocation Based on Fuzzy Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
You Wu ◽  
Zheng Wang ◽  
Shengqi Wang
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Business Process ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Data Mining Algorithm ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Database Technology

Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we proposed an incremental association rule mining algorithm based on clustering, that is, using fast clustering. First, the feasibility of realizing performance appraisal data mining is studied; then, the business process needed to realize the information system is analyzed, the business process-related links and the corresponding data input interface are designed, and then the data process to realize the data processing is designed, including data foundation and database model. Aiming at the high efficiency of large-scale database mining, database development tools are used to implement the specific system settings and program design of this algorithm. Incorporated into the human resource management system of colleges and universities, they carried out successful association broadcasting, realized visualization, and finally discovered valuable information.

Research on the Application of Improved Association Rule Algorithm in Supply Chain Management

2010 ◽  
Vol 121-122 ◽  
pp. 309-313
Author(s):  
Fang Jiang ◽  
Jie Zhao ◽  
Han Lin Ge
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Association Rule ◽  
Cost Effective ◽  
Data Mining Algorithm ◽  
Chain Management ◽  
Competitive Position ◽  
Production Processes ◽  
Mining Algorithm

Effective Supply Chain Management (SCM) approach must focus on flexible supply and production processes as well as rapidly respond to change of customer demands. To make up of existing drawbacks of association rule data mining algorithm, the paper brought out an improved algorithm and applies it in the product relativity analysis of SCM. Based on the algorithm, the solution of how the parts be arranged can achieve more cost-effective and higher profits can be achieved by data mining. The mining result can not only guide customers to correctly shopping, but also help manufacturers to design and produce goods, so that the companies can be in a better competitive position.

Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items

2006 ◽  
Vol 05 (03) ◽  
pp. 243-257
Author(s):  
R. B. V. Subramanyam ◽  
A. Goswami
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Initial Level ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Incremental Mining ◽  
Mining Algorithm ◽  
The Status ◽  
Dynamic Databases ◽  
Mining Algorithms

Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.

scholarly journals Data Mining Technology in Book Copyright Information Management Decision System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Song Lifang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Management ◽  
Association Rule ◽  
Information Management System ◽  
Management Decision ◽  
Apriori Algorithm ◽  
Mining Technology ◽  
Copyright Information ◽  
Rule Method

Today is an era of data “big bang”; Internet information technology is widely used in various fields of society. As an indispensable spiritual food in people’s daily life, books are increasing in number and scale. In order to better manage book information, people have introduced data mining technology. Based on this, this article takes the research and application of data mining technology in book copyright information management decision-making system as the theme, explores the role of data mining technology in book copyright information management, and aims to provide reference for our country’s book copyright information management and decision-making. This article first introduces the common algorithms of data mining technology and then elaborates on the advantages and effectiveness of the association rule method in data mining. Aiming at some defects of the original Apriori algorithm of the association rule method, an improved Apriori algorithm is proposed. After taking the library book information management system and database of a university in our province as the experimental research object, the performance gap between the two algorithms is compared through experiments, and it is concluded that when the number of transaction set item records is less than 1400, the Apriori algorithm performs better, and when the number of records in the transaction set is greater than 1400, the improved Apriori algorithm is obviously more advantageous. The research results show that the introduction and application of data mining technology make the information management of books more efficient and convenient, and it is more convenient for the management and decision-making of book copyright information.

Research and Application of an Enhanced Data Mining Algorithm in Virtual Manufacturing Technology

2011 ◽  
Vol 299-300 ◽  
pp. 840-843
Author(s):  
Yu Jun Tong ◽  
Jun Zhou ◽  
Wen Ge Xie ◽  
Dan Jia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Manufacturing Technology ◽  
Virtual Manufacturing ◽  
Data Mining Algorithm ◽  
Apriori Algorithm ◽  
Association Rules Mining ◽  
Mining Algorithm ◽  
Multiple Minimum Supports ◽  
Important Branch

Association rules mining is an important branch of data mining. Apriori algorithm is a classical algorithm of mining association rules. Based on the original Apriori algorithm an improved Apriori algorithm is analyzed according to the multiple minimum supports and support difference constraint. An experiment has been conducted and the results showed that the new algorithm can not only mine out the association rules to meet the demands of multiple minimum supports, but also mine out the rare but potentially profitable items’ association rules.

A Research for Data Mining Technology Based on Fuzzy Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 2509-2512 ◽  
Author(s):  
Li Na Liu ◽  
Hui Juan Qi ◽  
De Xiong Li
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Complex Structure ◽  
Data Mining Algorithm ◽  
Mining Technology ◽  
Training Time ◽  
Mining Algorithm ◽  
Fuzzy Neural

This paper introduces the concept of data mining generally and summarizes several methods of data mining, and presents a data mining algorithm based on fuzzy neural network (FNN). Using fuzzy theory and neural network to structure and train fuzzy neural network, the algorithm overcomes the shortcomings of neural network such as complex structure, long training time and lack of understandable representation of results.

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