scholarly journals Descubrimiento de Reglas de Asociación de eventos eruptivos del volcán Galeras [Discovery of Association Rules from eruptive events of Galeras volcano]

2015 ◽  
Author(s):  
Ricardo Timarán Pereira ◽  
Lisbeth Rosero Legarda ◽  
Yehimy Cabrera Cabrera
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Repository ◽  
Government Agencies ◽  
Disaster Prevention ◽  
Research Project ◽  
Data Mining Techniques ◽  
First Results ◽  
Seismological Observatory

En este artículo se presentan los primeros resultados del proyecto de investigación que tuvo como objetivo detectar patrones de eventos eruptivos del volcán Galeras con técnicas de minería de datos, a partir de los datos almacenados en el Observatorio Vulcanológico y Sismológico de Pasto - OVSP (Colombia), aplicando la metodología CRISP-DM. Se construyó, limpió y transformó un repositorio de datos con la información de los eventos eruptivos del volcán Galeras registrados desde 1989 hasta 2013. A partir de este repositorio, se detectaron patrones asociados a estos eventos, utilizando la tarea de minería de datos asociación. El conocimiento generado se integrará al existente con el fin de ayudar al OVSP y a los organismos gubernamentales de prevención de desastres a tomar decisiones eficaces en lo relacionado a la implementación de planes de prevención ante una posible erupción del volcán Galeras.Palabras Clave: Patrones de Eventos Eruptivos, Volcán Galeras, Minería de DatosIn this paper, the first results of a research project that aimed to detect patterns of eruptive events of Galeras volcano with data mining techniques from the data stored in the Volcanological and Seismological Observatory of Pasto - VSOP (Colombia), applying CRISP-DM methodology, are presented. A data repository with the information of the eruptive events of Galeras volcano recorded from 1989 to 2013 was built, cleaned and transformed. Using the data mining task association were detected patterns associated with these events. The knowledge generated will be integrated to the existing order to help VSOP and government agencies of disaster prevention to take effective decisions related to the implementation of prevention plans for a possible eruption of the Galeras volcano.Keywords: Patterns of eruptive events, Galeras volcano, Data Mining.

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.

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.

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.  

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.

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.

scholarly journals Worldwide co-occurrence analysis of 17 species of the genus Brachypodium using data mining

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6193 ◽  
Author(s):  
Simon Orozco-Arias ◽  
Ana María Núñez-Rincón ◽  
Reinel Tabares-Soto ◽  
Diana López-Álvarez
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Plant Ecology ◽  
Fundamental Aspect ◽  
Data Repository ◽  
Ecological Communities ◽  
Ecological Data ◽  
Global Biodiversity Information Facility ◽  
Ecological Processes ◽  
Wide Range

The co-occurrence of plant species is a fundamental aspect of plant ecology that contributes to understanding ecological processes, including the establishment of ecological communities and its applications in biological conservation. A priori algorithms can be used to measure the co-occurrence of species in a spatial distribution given by coordinates. We used 17 species of the genus Brachypodium, downloaded from the Global Biodiversity Information Facility data repository or obtained from bibliographical sources, to test an algorithm with the spatial points process technique used by Silva et al. (2016), generating association rules for co-occurrence analysis. Brachypodium spp. has emerged as an effective model for monocot species, growing in different environments, latitudes, and elevations; thereby, representing a wide range of biotic and abiotic conditions that may be associated with adaptive natural genetic variation. We created seven datasets of two, three, four, six, seven, 15, and 17 species in order to test the algorithm with four different distances (1, 5, 10, and 20 km). Several measurements (support, confidence, lift, Chi-square, and p-value) were used to evaluate the quality of the results generated by the algorithm. No negative association rules were created in the datasets, while 95 positive co-occurrences rules were found for datasets with six, seven, 15, and 17 species. Using 20 km in the dataset with 17 species, we found 16 positive co-occurrences involving five species, suggesting that these species are coexisting. These findings are corroborated by the results obtained in the dataset with 15 species, where two species with broad range distributions present in the previous dataset are eliminated, obtaining seven positive co-occurrences. We found that B. sylvaticum has co-occurrence relations with several species, such as B. pinnatum, B. rupestre, B. retusum, and B. phoenicoides, due to its wide distribution in Europe, Asia, and north of Africa. We demonstrate the utility of the algorithm implemented for the analysis of co-occurrence of 17 species of the genus Brachypodium, agreeing with distributions existing in nature. Data mining has been applied in the field of biological sciences, where a great amount of complex and noisy data of unseen proportion has been generated in recent years. Particularly, ecological data analysis represents an opportunity to explore and comprehend biological systems with data mining and bioinformatics tools.

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.

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