Interestingness Classification of Association Rules for Master Data

2017 ◽  
pp. 237-245 ◽  
Author(s):  
Wei Han ◽  
Julio Borges ◽  
Peter Neumayer ◽  
Yong Ding ◽  
Till Riedel ◽  
...  
Keyword(s):  
Association Rules ◽  
Master Data

scholarly journals Prediction of Blood Donors Population Using Data Mining Classification Technique

2021 ◽  
pp. 25-27
Author(s):  
Aman Paul ◽  
Daljeet Singh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Association Rules ◽  
User Interfaces ◽  
Relational Data ◽  
Easy Access ◽  
Data Mining Technique ◽  
Open Source Data ◽  
Using Data

Data mining is a technique that finds relationships and trends in large datasets to promote decision support. Classification is a data mining technique that maps data into predefined classes often referred as supervised learning because classes are determined before examining data. Different classification algorithms have been proposed for the effective classification of data. Among others, Weka is an open-source data mining software with which classification can be achieved. It is also well suited for developing new machine learning schemes. It allows users to quickly compare different machine learning methods on new datasets. It has several graphical user interfaces that enable easy access to the underlying functionality. CBA is a data mining tool which not only produces an accurate classifier for prediction, but it is also able to mine various forms of association rules. It has better classification accuracy and faster mining speed. It can build accurate classifiers from relational data and mine association rules from relational data and transactional data. CBA also has many other features like cross validation for evaluating classifiers and allows the user to view and to query the discovered rules.

scholarly journals EDM – survey of performance factors and algorithms applied

2018 ◽  
Vol 7 (2.6) ◽  
pp. 93 ◽  
Author(s):  
Deepali R Vora ◽  
Kamatchi Iyer
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Educational Data Mining ◽  
Knowledge Discovery In Databases ◽  
Useful Knowledge ◽  
Performance Factors ◽  
Student’S Performance

Educational Data Mining (EDM) is a new field of research in the data mining and Knowledge Discovery in Databases (KDD) field. It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities. Analysing students’ data and information to perform various tasks like classification of students, or to create decision trees or association rules, so as to make better decisions or to enhance student’s performance is an interesting field of research. The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same. The paper identifies the lacuna and challenges in Algorithms applied, Performance Factors considered and data used in EDM.

scholarly journals Classifiying English Books By Using Association Rules

2018 ◽  
Vol 225 (3) ◽  
pp. 305-324
Author(s):  
Asist .instructor. Enas Jasim Hadi
Keyword(s):  
Association Rules ◽  
Error Rate ◽  
Educational Institutions ◽  
Efficient Manner ◽  
Knowledge Resources ◽  
Library Resources ◽  
Computer Based ◽  
The Right

     In general, libraries have taken a special interest from various educational institutions due to the key role that libraries play in terms of enriching the scientific aspects with different knowledge resources. The role of libraries is significantly can be seen in the fast information delivery for the recipients. Hence, the indexing and classifying of library resources is very important task because it contributes in enhancing the libraries performance. The classification of books (putting the book in the right class) is not an easy task, especially when the books are written in English. This research presents a model for books classification by using Association Rules techniques. The first stage of the research involved distributing a questionnaire for people who are working in libraries. The purpose of the questionnaire was to identify the difficulties that come with English books classification. The results of the questionnaire proved that there were number of obstacles in terms of English books classification process. After that we have collected an English books dataset to be employed in our model. Then the model has been implemented successfully and tested by given the title of the book. The results of the research proved that the model can put the book into the right class with ratio of (76%) and the error rate was (24%). That means it is possible to develop computer-based systems to classify the library resources. The results of the research open the way toward developing more sophisticated techniques and methods which can improve the performance of classifying the books electronically in an efficient manner that serves the librarians and users as well.

scholarly journals Research on Classification of Tibetan Medical Syndrome in Chronic Atrophic Gastritis

2019 ◽  
Vol 9 (8) ◽  
pp. 1664 ◽  
Author(s):  
Xiaolan Zhu ◽  
Lei Zhang ◽  
Yuan Zhang ◽  
Lu Wang ◽  
Shiying Wang ◽  
...  
Keyword(s):  
Atrophic Gastritis ◽  
Association Rules ◽  
Chronic Atrophic Gastritis ◽  
Classification Model ◽  
Apriori Algorithm ◽  
Time Cost ◽  
Medical Clinics ◽  
Traditional Classification ◽  
The Common

Classification association rules that integrate association rules with classification are playing an important role in data mining. However, the time cost on constructing the classification model, and predicting new instances, will be long, due to the large number of rules generated during the mining of association rules, which also will result in the large system consumption. Therefore, this paper proposed a classification model based on atomic classification association rules, and applied it to construct the classification model of a Tibetan medical syndrome for the common plateau disease called Chronic Atrophic Gastritis. Firstly, introduce the idea of “relative support”, and use the constraint-based Apriori algorithm to mine the strong atomic classification association rules between symptoms and syndrome, and the knowledge base of Tibetan medical clinics will be constructed. Secondly, build the classification model of the Tibetan medical syndrome after pruning and prioritizing rules, and the idea of “partial classification” and “first easy to post difficult” strategy are introduced to realize the prediction of this Tibetan medical syndrome. Finally, validate the effectiveness of the classification model, and compare with the CBA algorithm and four traditional classification algorithms. The experimental results showed that the proposed method can realize the construction and classification of the classification model of the Tibetan medical syndrome in a shorter time, with fewer but more understandable rules, while ensuring a higher accuracy with 92.8%.

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