scholarly journals Study of the Behavior of Cryptocurrencies in Turbulent Times Using Association Rules

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1620
Author(s):  
José Benito Hernández C. ◽  
Andrés García-Medina ◽  
Miguel Andrés Porro V.
Keyword(s):  
Time Series ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Data Set ◽  
Turbulent Times

We studied the effects of the recent financial turbulence of 2020 on the cryptocurrency market, taking into account both prices and volumes from December 2019 to July 2020. Time series were transformed into transaction matrices, and the Apriori algorithm was applied to find the association rules between different currencies, identifying whether the price or the volume of the currencies compose the rules. We divided the data set into two subsets and found that before the decline in cryptocurrency prices, the association rules were generally formed by these prices and that, then, the volumes of the transactions dominated to form the association rules.

scholarly journals Improved Classification Techniques to Predict the Co-disease in Diabetic Mellitus Patients using Discretization and Apriori Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 730-733
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Census Data ◽  
Early Stage ◽  
Research Work ◽  
Numerical Data ◽  
Medical Data ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Data Set

The demand for data mining is now unavoidable in the medical industry due to its various applications and uses in predicting the diseases at the early stage. The methods available in the data mining theories are easy to extract the useful patterns and speed to recognize the task based outcomes. In data mining the classification models are really useful in building the classes for the medical data sets for future analysis in an accurate way. Besides these facilities, Association rules in data mining are a promising technique to find hidden patterns in a medical data set and have been successfully applied with market basket data, census data and financial data. Apriori algorithm, is considered to be a classic algorithm, is useful in mining frequent item sets on a database containing a large number of transactions and it also predicts the relevant association rules. Association rules capture the relationship of items that are present in data sets and when the data set contains continuous attributes, the existing algorithms may not work due to this, discretization can be applied to the association rules in order to find the relation between various patterns in data set. In this paper of our research, using Discretized Apriori the research work is done to predict the by-disease in people who are found with diabetic syndrome; also the rules extracted are analyzed. In the discretization step, numerical data is discretized and fed to the Apriori algorithm for better association rules to predict the diseases.

scholarly journals Google Summer of Code Gender Diversity: An analysis of the last 4 editions

2021 ◽  
Author(s):  
Jhemeson Silva Mota ◽  
Marcio Vinicius Okimoto ◽  
Edna Dias Canedo ◽  
Jhonatan Silva Mota
Keyword(s):  
Association Rules ◽  
Data Science ◽  
Gender Diversity ◽  
Apriori Algorithm ◽  
Web Development ◽  
Event Management ◽  
Data Set ◽  
Men And Women ◽  
Development Data ◽  
Gender Characteristics

This work presents a comprehensive research about the participationof men and women in the area of Information and CommunicationsTechnology (ICT) through data extracted from the last foureditions of Google Summer of Code (GSoC). The goal of this workis to find Association Rules between gender characteristics andcoding using the Apriori Algorithm. A total of 61 association ruleswere generated through the aforementioned algorithm, being 22 ofthem found only in the data set with the women, 24 found only withthe men, and 15 applicable to both sets. We can cite as one of themain findings of this work the fact that the representativeness ofwomen in GSoC is decreasing in the last few years. Despite this, therepresentativeness of women in GSoC is above average, accordingto what has been reported in other studies in the literature in whichwomen are underrepresented. When it comes to the most utilizedtechnologies, we have “Python", “Java", “C++", “C" and “JavaScript"in the top. Analyzing technologies, it’s possible to realize that themain utilized technologies for men and women are similar, but, ingeneral, men are more likely linked to programming languages.The most common project topics are: “Event Management", “Web",“Web Development", “Data Science" and “Cloud" in the top. Thiscan represent how diverse the project topics of the database are,but not necessarily has something related to gender.

scholarly journals Improved Classification Techniques to Predict the Co-disease in Diabetic Mellitus Patients using Discretization and Apriori Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 730-733
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Census Data ◽  
Early Stage ◽  
Research Work ◽  
Numerical Data ◽  
Medical Data ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Data Set

The demand for data mining is now unavoidable in the medical industry due to its various applications and uses in predicting the diseases at the early stage. The methods available in the data mining theories are easy to extract the useful patterns and speed to recognize the task based outcomes. In data mining the classification models are really useful in building the classes for the medical data sets for future analysis in an accurate way. Besides these facilities, Association rules in data mining are a promising technique to find hidden patterns in a medical data set and have been successfully applied with market basket data, census data and financial data. Apriori algorithm, is considered to be a classic algorithm, is useful in mining frequent item sets on a database containing a large number of transactions and it also predicts the relevant association rules. Association rules capture the relationship of items that are present in data sets and when the data set contains continuous attributes, the existing algorithms may not work due to this, discretization can be applied to the association rules in order to find the relation between various patterns in data set. In this paper of our research, using Discretized Apriori the research work is done to predict the by-disease in people who are found with diabetic syndrome; also the rules extracted are analyzed. In the discretization step, numerical data is discretized and fed to the Apriori algorithm for better association rules to predict the diseases.

scholarly journals Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6889
Author(s):  
Yuxin Huang ◽  
Jingdao Fan ◽  
Zhenguo Yan ◽  
Shugang Li ◽  
Yanping Wang
Keyword(s):  
Association Rules ◽  
Early Warning ◽  
Association Rule ◽  
Cluster Center ◽  
Apriori Algorithm ◽  
Data Set ◽  
Early Warning Model ◽  
Initial Cluster ◽  
Different Dimensions ◽  
Warning Model

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.

Review and comparison of Apriori algorithm implementations on Hadoop-MapReduce and Spark

2018 ◽  
Vol 33 ◽  
Author(s):  
Eduardo P. S. Castro ◽  
Thiago D. Maia ◽  
Marluce R. Pereira ◽  
Ahmed A. A. Esmin ◽  
Denilson A. Pereira
Keyword(s):  
Association Rules ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Mapreduce Framework ◽  
Data Set ◽  
Hadoop Mapreduce ◽  
Detailed Assessment ◽  
Mining Association Rules

AbstractSeveral Apriori algorithm implementations for mining association rules have been proposed in the literature using the Hadoop-MapReduce framework and, more recently, Spark. However, none of the works have made a detailed assessment of its performance, for example, comparing it with other implementations in various characteristics of data sets. In this work, we present a review of the main algorithms proposed for Hadoop-MapReduce and compared their implementations in a single environment under several different situations. Moreover, these algorithms had their implementations adapted to Spark, and also compared under the same circumstances. Based on the results of the experiments, we present a framework for recommending the Apriori implementation most appropriate for solving a given problem, according to the data set characteristics and minimum required support. The results show that Spark implementations overcome Hadoop-MapReduce implementations at runtime in most experiments. However, there is no single implementation that is the best in all the evaluated situations.

scholarly journals Analysis and prediction of student placement for improving the education standards

2018 ◽  
Vol 7 (2.8) ◽  
pp. 303
Author(s):  
S Anjali Devi ◽  
M Vishnu Priya ◽  
P Akhila ◽  
N Vasundhara
Keyword(s):  
Academic Success ◽  
Association Rules ◽  
Evaluation System ◽  
Educational Institutions ◽  
Apriori Algorithm ◽  
Education Standards ◽  
Data Set ◽  
Student Placement ◽  
Conventional Methods ◽  
Academic Information

Students’ academic success can be evaluated based on their performance in the exams conducted by the institutions. In this paper, we propose a scheme where prediction of student final placement can be done based on the marks scored by them in the previous semesters. In order to predict the placement of the student we need some data to analyze. For this purpose we will supply students basic details and their previous academic information into the system which will be used to predict the placement of the student. This is done by generating association rules using apriori algorithm. Admin and user will use this system. Here user will be the student. Admin and user will use their login to access the system. Admin will add academic details of the students, like their SSC, HSC, Graduation marks (up to current semester, Back logs etc.,). User will be the student. Admin and user will use their login to access the system. Admin will add academic details of the students, like their SSC, HSC, Graduation marks (up to current semester, Back logs etc.,).  This system can be used in schools, colleges and other educational institutions. This evaluation system is more accurate than other conventional methods. We are using a university data set to predict the placement of the student.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

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