Differential Association Rules

2008 ◽  
pp. 1747-1758
Author(s):  
Christopher Besemann ◽  
Anne Denton ◽  
Ajay Yekkirala ◽  
Ron Hutchison ◽  
Anderson Marc
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Interaction Networks ◽  
Differential Association ◽  
Interacting Proteins ◽  
Data Mining Techniques

In this chapter, we discuss the use of differential association rules to study the annotations of proteins in one or more interaction networks. Using this technique, we find the differences in the annotations of interacting proteins in a network. We extend the concept to compare annotations of interacting proteins across different definitions of interaction networks. Both cases reveal instances of rules that explain known and unknown characteristics of the network(s). By taking advantage of such data mining techniques, a large number of interesting patterns can be effectively explored that otherwise would not be.

Differential Association Rules

2011 ◽  
pp. 269-282
Author(s):  
Christopher Besemann ◽  
Anne Denton ◽  
Ajay Yekkirala ◽  
Ron Hutchison ◽  
Marc Anderson
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Interaction Networks ◽  
Differential Association ◽  
Interacting Proteins ◽  
Data Mining Techniques

In this chapter, we discuss the use of differential association rules to study the annotations of proteins in one or more interaction networks. Using this technique, we find the differences in the annotations of interacting proteins in a network. We extend the concept to compare annotations of interacting proteins across different definitions of interaction networks. Both cases reveal instances of rules that explain known and unknown characteristics of the network(s). By taking advantage of such data mining techniques, a large number of interesting patterns can be effectively explored that otherwise would not be.

Differential Association Rules

2011 ◽  
pp. 620-631
Author(s):  
Christopher Besemann ◽  
Anne Denton ◽  
Ajay Yekkirala ◽  
Ron Hutchison ◽  
Mark Anderson
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Interaction Networks ◽  
Differential Association ◽  
Interacting Proteins ◽  
Data Mining Techniques

In this chapter, we discuss the use of differential association rules to study the annotations of proteins in one or more interaction networks. Using this technique, we find the differences in the annotations of interacting proteins in a network. We extend the concept to compare annotations of interacting proteins across different definitions of interaction networks. Both cases reveal instances of rules that explain known and unknown characteristics of the network(s). By taking advantage of such data mining techniques, a large number of interesting patterns can be effectively explored that otherwise would not be.

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

The Application of Apriori Algorithm in Analysis on Admitted Students of Colleges and Universities

2013 ◽  
Vol 321-324 ◽  
pp. 2578-2582
Author(s):  
Qian Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Minimum Support ◽  
Sample Data ◽  
Mining Association Rules

This paper examined the application of Apriori algorithm in extracting association rules in data mining by sample data on student enrollments. It studied the data mining techniques for extraction of association rules, analyzed the correlation between specialties and characteristics of admitted students, and evaluated the algorithm for mining association rules, in which the minimum support was 30% and the minimum confidence was 40%.

scholarly journals Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Asep Budiman Kusdinar ◽  
Daris Riyadi ◽  
Asriyanik Asriyanik
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
Data Mining Techniques ◽  
Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While  the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is  to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.  

A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences Based on Association Rules

2011 ◽  
pp. 231-247
Author(s):  
Peng-Yeng yin ◽  
Shyong-Jian Shyu ◽  
Guan-Shieng Huang ◽  
Shuang-Te Liao
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Phylogenetic Analyses ◽  
Clustering Algorithms ◽  
Protein Sequences ◽  
Bayesian Framework ◽  
Biological Data ◽  
Data Mining Techniques ◽  
Analysis Task ◽  
Using Data

With the advent of new sequencing technology for biological data, the number of sequenced proteins stored in public databases has become an explosion. The structural, functional, and phylogenetic analyses of proteins would benefit from exploring databases by using data mining techniques. Clustering algorithms can assign proteins into clusters such that proteins in the same cluster are more similar in homology than those in different clusters. This procedure not only simplifies the analysis task but also enhances the accuracy of the results. Most of the existing protein-clustering algorithms compute the similarity between proteins based on one-to-one pairwise sequence

scholarly journals Applications of Data Mining in Mitigating Fire Accidents Based on Association Rules

2021 ◽  
Vol 15 (12) ◽  
pp. 158
Author(s):  
Ibrahim Nasir Mahmood ◽  
Hussein Ali Aliedane ◽  
Mustafa Ali Abuzaraida
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Cleaning ◽  
Data Exploration ◽  
Occurrence Rate ◽  
Data Mining Techniques ◽  
Hidden Knowledge ◽  
Applied Approach ◽  
Fire Accidents ◽  
Damages And Losses

<p class="0abstract">Due to the increased rate of fire accidents which cause many damages and losses to people souls, material, and property in Basra city. The necessity of analyzing and mining the data of the fire accidents became an urgent need to find a solution. The need increased for a solution that helps to mitigate and reduce the number of accidents. In this paper, data mining techniques and applications including data preprocessing, data cleaning, and data exploration have been applied. Data mining applications is performed to analyze and discover the hidden knowledge in ten years of data (fire accidents happened from 2010 – 2019) which is approximately 20k record of accidents. These data mining techniques along with the association rules algorithm is applied on the dataset. The applied approach and techniques resulted in discovering the patterns and the nature of the fire accidents in Basra city. It also helped to reach to recommendations and resolutions for mitigating the fire accidents and its occurrence rate.</p>

scholarly journals Analysis of flights’ delays using association rules

2018 ◽  
Vol 19 (12) ◽  
pp. 755-757
Author(s):  
Mariusz Dramski ◽  
Marcin Mąka
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Department Of Transportation ◽  
Passenger Transport ◽  
Data Mining Techniques ◽  
The Us

The efficiency of air passenger transport in world's economy is crucial. For this kind of flights, one of the most important features is punctuality. The network of connections between the airports, very often is significantly complicated. It leads to the conclusion that there is a need to do some research in this field which will help the passengers to plan their optimal journeys. In this paper one of the data mining techniques (association rules) was applied to the analysis of flights' delays. The data consists of over 7 millions records was taken from the US Department of Transportation (year 2008) [2]. Then the re-search was carried out and conclusions were described.

Hiding Sensitive Association Rules by Sanitizing

2013 ◽  
Vol 694-697 ◽  
pp. 2317-2321
Author(s):  
Hui Wang
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Mining Technology ◽  
Data Mining Techniques ◽  
Confidential Data ◽  
Knowledge Hiding ◽  
Existing Data

The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.

scholarly journals Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Elayne Rubio Delgado ◽  
Lisbeth Rodríguez-Mazahua ◽  
José Antonio Palet Guzmán ◽  
Jair Cervantes ◽  
José Luis Sánchez Cervantes ◽  
...  
Keyword(s):  
Data Mining ◽  
Natural Language ◽  
Bayesian Networks ◽  
Association Rules ◽  
Web Application ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Factors Influencing ◽  
New Knowledge

This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.

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