An efficient data mining technique for discovering interesting association rules

2002 ◽  
Author(s):  
Show-Jane Yen ◽  
A.L.P. Chen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Efficient Data

scholarly journals Efficient Data Mining Techniques to Improve Academic Performance of Students

2021 ◽  
Vol 13 (SUP 1) ◽  
pp. 81-84
Author(s):  
Rashmi V. Varade ◽  
Blessy Thankanchan
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Large Volume ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Efficient Data

Predicting the academic performance of students is very challenging due to large volume of data in the educational institutions database. Data mining techniques are implemented to predict students' academic performance in many institutions. Because of predicting students' performance, it will help teachers and institutions to decide strategies to teach to the students who are weak in studies and also they can define different strategies who are good in studies so that these students can perform better, So, aim of this paper is to study such a data mining technique which will help us to predict students' academic performance in advance.

From data to knowledge mining

2009 ◽  
Vol 23 (4) ◽  
pp. 427-441 ◽  
Author(s):  
Ana Cristina Bicharra Garcia ◽  
Inhauma Ferraz ◽  
Adriana S. Vivacqua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Evaluation Criteria ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Points

AbstractMost past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.

Rough Set Theory and Fuzzy Logic Based Warehousing of Heterogeneous Clinical Databases

2017 ◽  
Vol 25 (03) ◽  
pp. 385-408 ◽  
Author(s):  
R. Saravana Kumar ◽  
G. Tholkappia Arasu
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Second Phase ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Clinical Databases ◽  
Efficient Data

Large amounts of data about the patients with their medical conditions are presented in the Medical databases. Analyzing all these databases is one of the difficult tasks in the medical environment. In order to warehouse all these databases and to analyze the patient’s condition, we need an efficient data mining technique. In this paper, an efficient data mining technique for warehousing clinical databases using Rough Set Theory (RST) and Fuzzy Logic is proposed. Our proposed methodology contains two phases – (i) Clustering and (ii) Classification. In the first phase, Rough Set Theory is used for clustering. Clustering is one of the data mining techniques for warehousing the heterogeneous data bases. Clustering technique is used to group data that have similar characteristics in the same cluster and also to group the data that have dissimilar characteristics with other clusters. After clustering the data, similar objects will be clustered in one cluster and the dissimilar objects will be clustered under another cluster. The RST can be reduced the complexity. Then in the second phase, these clusters are classified using Fuzzy Logic. Normally, Classification with Fuzzy Logic is generated more number of rules. Since the RST is utilized in our work, the classification using Fuzzy can be done with less amount of complexity. The proposed approach is evaluated using various clinical related databases from heart disease datasets – Cleveland, Switzerland and Hungarian. The performance analysis is based on Sensitivity, Specificity and Accuracy with different cluster numbers. The experimentation results show that our proposed methodology provides better accuracy result.

scholarly journals Career and Skills Recommendations using Data Mining Technique: Matching Right People for Right Profession, in Pakistani Context

2018 ◽  
pp. 93-101
Author(s):  
◽  
◽  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Educational Background ◽  
The Internet ◽  
Job Seekers ◽  
Data Mining Technique ◽  
Association Rules Mining ◽  
Mining Technique ◽  
Using Data ◽  
Different Sources

There are a number of recommendation systems available on the internet for the help of jobseekers. These systems only generate job recommendations for people on the basis of input entered by user. The problem observed in Pakistani people is they are not clear in which field they should start or switch working. Before searching and applying for a job, one should be clear about his/her profession and important skills regarding selected profession. Based on above issues, there is a need to design such a system that can overcome the problem of profession selection and skills suggestions so that it can be easy for a jobseeker to apply for a specific job. In this research, the problem which is discussed above is resolved by proposing a model by using Association Rules Mining, a data mining technique. In this model, professions are recommended to job seekers by matching the profile of applicant or job seeker with those persons who have same profile like educational background, professional skills and the type of jobs which they are doing. The data collected for this research itself is a major contribution as we collected it from different sources. We will make this data publically available for others so that they can use for further research.

scholarly journals IMPLEMENTASI DATA MINING DALAM ANALISA POLA PEMINJAMAN BUKU DI PERPUSTAKAAN MENGGUNAKAN METODE ASSOCIATION RULE

JURTEKSI ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 89-96
Author(s):  
Edi Kurniawan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Growth Data ◽  
Data Mining Technique ◽  
Transaction Data ◽  
Mining Technique ◽  
New Information ◽  
Important Means ◽  
Mining Association Rule

Abstract: The library is one of the most important means to add insight and knowledge to everyone. In general, borrowing transaction data books that exist in a library are only left to accumulate by the library in the database without any utilization or further processing of the data that has long been stored. By utilizing the Data Mining technique using association rules with FP-Growth, these data will be very useful. Because from the data lending books to the library, new information can be gleaned about what books are often borrowed and know the pattern of relationships between books that have been borrowed together so that later it can be used to compile books in accordance with the existing borrowing patterns so that they can facilitate library visitors in the process of finding books. Keywords: Data Mining, Association Rule, FP-Growth, Library Abstrak: Perpustakaan merupakan salah satu sarana yang sangat penting untuk menambah wawasan dan keilmuan setiap orang. Pada umumnya data transaksi peminjaman buku yang ada pada sebuah perpustakaan hanya dibiarkan saja menumpuk oleh pihak perpustakaan di dalam database tanpa ada pemanfaatan atau pengolahan lebih lanjut dari data-data yang telah lama tersimpan tersebut. Dengan melakukan pemanfaatan menggunakan Teknik Data Mining metode association rules dengan FP-Growth, data-data tersebut akan jadi sangat bermanfaat. Karena dari data peminjaman buku pada perpustakaan tersebut dapat diggali informasi baru tentang buku-buku apa yang sering dipinjam dan mengetahui pola hubungan antara buku yang telah dipinjam secara bersama-sama sehingga nantinya dapat dimanfaatkan untuk melakukan penyusunan buku sesuai dengan pola peminjaman buku yang ada sehingga dapat mempermudah para pengunjung perpustakaan dalam proses pencarian buku. Kata Kunci : Data Mining, Asociation Rule, FP-Growth, Perpustakaan

Sign in / Sign up

Export Citation Format

Share Document