An Improved Data Mining Method Applied to Enterprise’s Financial

2010 ◽  
Vol 143-144 ◽  
pp. 477-481 ◽  
Author(s):  
Yin Qiu Wang ◽  
Xun Xu
Keyword(s):  
Data Mining ◽  
Economic Efficiency ◽  
Financial Management ◽  
Business Management ◽  
Financial Statements ◽  
Experimental Results ◽  
Enterprise Management ◽  
Mining Method ◽  
The Core ◽  
Data Mining Method

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. The financial management of enterprise management is an important component of the work is the core of enterprise management, improve business management and enhance the economic efficiency of enterprises is very important role. This paper proposes an improved data mining method to enhance the capability of exploring valuable information from financial statements. Experimental results indicate that this proposed method significantly improves the performance.

Research and Simulation of Data Mining Method Based on the Association Rules Algorithm

2014 ◽  
Vol 651-653 ◽  
pp. 2185-2188
Author(s):  
Jin Ping Zou ◽  
Xiao Dong Xie
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rules ◽  
Experimental Results ◽  
Mining Method ◽  
Accurate Data ◽  
Minimum Support ◽  
Data Mining Method

the accurate data mining problem is studied in this paper. With the increasing of data attributes, degree of complexity of the data storage is also increased, resulting in that in data mining process, the complexity of computation is too high, reducing the convergence of the data mining method, thereby reducing the efficiency of data mining. To this end, this paper presents a data mining method based on association rules algorithm. The data is made simplified processing, to obtain the association rules between data which provides the basis for data mining. According to the association rules between the data, the data in line with the minimum support degree is calculated, to achieve accurate data mining. Experimental results show that the proposed algorithm for data mining, can improve mining efficiency, and achieve the desired results.

An Data Ming Method Based on AHP and Apriori

2012 ◽  
Vol 198-199 ◽  
pp. 431-434
Author(s):  
Hua Lin Ma
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
The Internet ◽  
Mining Method ◽  
Data Mining Method ◽  
Data Ming

As the current personalized recommendation methods of Internet bookstore are limited too much in function, this paper proposes a kind of Internet bookstore data mining method based on “Strategic”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. The experimental results indicate that the Internet bookstore recommendation method is feasible.

Key Data Optimization Mining in Massive Student Database Model

2014 ◽  
Vol 687-691 ◽  
pp. 1466-1469
Author(s):  
Zhen Chao Wang
Keyword(s):  
Data Mining ◽  
Traditional Method ◽  
Experimental Results ◽  
Redundant Information ◽  
Mining Method ◽  
Information Mining ◽  
Database Model ◽  
Data Mining Method ◽  
Mining Model ◽  
Effective Cluster

In the process of massive student data mining using traditional method, special words and related characteristics were used as mining objects. The concealment and feature of deliberately camouflaged of information made it is difficult for mining model to form an effective cluster centers, which reduced the accuracy of information mining. Hence an optimized data mining method was proposed. According to the degree of generalization and fuzziness of the feature words of student, the threshold of mining information was set, which avoided the effects of redundant information, thus the efficiency of mining was improved. The experimental results showed that using the improved algorithm to perform information mining in massive student database could effectively improve mining efficiency.

scholarly journals Model Prediksi Penempatan Magang Siswa SMK menggunakan Teknik Association Rule Mining

2020 ◽  
Vol 9 (1) ◽  
pp. 1-10
Author(s):  
Dwi Welly Sukma Nirad ◽  
Afriyanti Dwi Kartika ◽  
Aghill Tresna Avianto ◽  
Aulia Anshari Fathurrahman
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Association Rule ◽  
Association Rule Mining ◽  
Vocational School ◽  
Mining Method ◽  
Rule Mining ◽  
The Core ◽  
Data Mining Method ◽  
The Future

Insternship activity is one of the core activities of every Vocational School (SMK) as the purpose of this school is to conduct education at the level of work-oriented readiness. Every SMK graduate is expected to be better prepared to enter the industrial world. However, in fact there were gaps that resulted in the unpreparedness of students after graduating from school. This research identified and analyzed the placement of student internships. The aim was to find an insternship placement pattern in order to get an overview and recommendation of an appropriate internship according to students abilities. The technique used was the association rule mining, a technique of the data mining method that was useful for uncovering the rules that were correlated to each other so that they can better organize and predict the internship placements. The results showed that the association rule mining could be applied to analyze student performance and predict internship placements in the future. This prediction could be a consideration for the teacher to determine the subjects that need to be improved to prepare students for internships.

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