scholarly journals AUMENTANDODESEMPENHO DEALGORITMOSDEMINERAÇÃODEDADOSUTILIZANDOAPLATAFORMACUDA

2018 ◽  
pp. 90-102
Author(s):  
Matheus Varela Ferreira ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Data Processing ◽  
Processing Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Short Term ◽  
Data Mining Algorithms ◽  
Mining Algorithms

With the increasing need to make decisions in the short term, industry (pharmaceutical, petrochemical, aeronautics and etc.) has been seeking new ways to reduce the time of the data mining process to obtain knowledge. In recent years, many technological resources are being used to mitigate this need, an example is CUDA. CUDA is a platform that enables the use of GeForce GPUs in conjunction with CPUs for data processing, significantly reducing processing time. This work proposes to perform a comparative analysis of the processing time between two versions of some data mining algorithms (Apriori, AprioriAll, Naïve Bayes and K-Means), one running on CPU only and one on CPU in conjunction with GPU through platform CUDA. Through the experiments performed, it was observed that using the CUDA platform it is possible to obtain satisfactory results.

scholarly journals Comparison of Data Mining Algorithms Using Artificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction

2020 ◽  
Vol 1641 ◽  
pp. 012068
Author(s):  
Diah Puspitasari ◽  
Kresna Ramanda ◽  
Adi Supriyatna ◽  
Mochamad Wahyudi ◽  
Erma Delima Sikumbang ◽  
...  
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Preterm Birth ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithms ◽  
Artificial Neural ◽  
Mining Algorithms

scholarly journals Migrating From Data Mining to Big Data Mining

2018 ◽  
Vol 7 (3.4) ◽  
pp. 13
Author(s):  
Gourav Bathla ◽  
Himanshu Aggarwal ◽  
Rinkle Rani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Response Time ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Big Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with  pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples.  Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.  

scholarly journals Predictive Factors of Infant Mortality Using Data Mining in Iran

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mahmoud Hajipour ◽  
Niloufar Taherpour ◽  
Haleh Fateh ◽  
Ebrahim Yousefi ◽  
Koorosh Etemad ◽  
...  
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Infant Mortality ◽  
Rural Areas ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Objectives: Reducing infant mortality in the whole world is one of the millennium development goals.The aim of this study was to determine the factors related to infant mortality using data mining algorithms. Methods: This population-based case-control study was conducted in eight provinces of Iran. A sum of 2,386 mothers (1,076 cases and 1,310 controls) enrolled in this study. Data were extracted from health records of mothers and filled with checklists in health centers. We employed several data mining algorithms such as AdaBoost classifier, Support Vector Machine, Artificial Neural Networks, Random Forests, K-nearest neighborhood, and Naïve Bayes in order to recognize the important predictors of infant death; binary logistic regression model was used to clarify the role of each selected predictor. Results: In this study, 58.7% of infant mortalities occurred in rural areas, that 55.6% of them were boys. Moreover, Naïve Bayes and Random Forest were highly capable of predicting related factors among data mining models. Also, the results showed that events during pregnancy such as dental disorders, high blood pressure, loss of parents, factors related to infants such as low birth weight, and factors related to mothers like consanguineous marriage and gap of pregnancy (< 3 years) were all risk factors while the age of pregnancy (18 - 35 year) and a high degree of education were protective factors. Conclusions: Infant mortality is the consequence of a variety of factors, including factors related to infants themselves and their mothers and events during pregnancy. Owing to the high accuracy and ability of modern modeling compared to traditional modeling, it is recommended to use machine learning tools for indicating risk factors of infant mortality.

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

scholarly journals Vertical Data Processing for Mining Big Data: A Predicate Tree Approach

10.29007/db8n ◽  
2019 ◽  
Author(s):  
Mohammad Hossain ◽  
Maninder Singh ◽  
Sameer Abufardeh
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Processing ◽  
Processing Time ◽  
Traditional Approach ◽  
Critical Factor ◽  
Boolean Operations ◽  
Data Mining Algorithms ◽  
Vertical Data ◽  
Big Data Application

Time is a critical factor in processing a very large volume of data a.k.a ‘Big Data’. Many existing data mining algorithms (supervised and unsupervised) become futile because of the ubiquitous use of horizontal processing i.e. row-by-row processing of stored data. Processing time for big data is further exacerbated by its high dimensionality (# of features) and high cardinality (# of records). To address this processing-time issue, we proposed a vertical approach with predicate trees (pTree). Our approach structures data into columns of bit slices, which range from few to hundreds and are processed vertically i.e. column by column. We tested and compared our vertical approach to traditional (horizontal) approach using three basic Boolean operations namely addition, subtraction and multiplication with 10 data sizes. The length of data size ranged from half a billion bits to 5 billion bits. The results are analyzed w.r.t processing speed time and speed gain for both the approaches. The result shows that our vertical approach outperformed the traditional approach for all Boolean operations (add, subtract and multiply) across all data sizes and results in speed-gain between 24% to 96%. We concluded from our results that our approach being in data-mining ready format is best suited to apply to operations involving complex computations in big data application to achieve significant speed gain.

scholarly journals Naive bayes algorithm performance for smartphone sentiment analysis in social media

2018 ◽  
Vol 1 (2) ◽  
pp. 76
Author(s):  
Monalisa Fatmawati Sarifah
Keyword(s):  
Communication Technology ◽  
Naive Bayes ◽  
Analytical Techniques ◽  
Naïve Bayes ◽  
Algorithm Performance ◽  
Data Mining Algorithms ◽  
Exchange Information ◽  
Learning Technique ◽  
Bayes Algorithm ◽  
Mining Algorithms

Indonesia with a population of 250 million is a large market, Millennials tend to be more adaptive to the development of communication technology [1]. There are lot of opportunities that are used by various groups, one of which is the need to use smartphones that can make it easier for people to exchange information [2].  The shift in sales of smartphone brands in Indonesia is influenced by  massive advertising carried out by smartphone vendors (smartphone capitalists) to consumers [3]. The enthusiasm of the community in welcoming this platform is so great, lot of comment about smartphone brand stated by public is an interesting thing to be processed to be information. Utilization of that information requires analytical techniques so that the produced information can help many parties. The method used in this study is Naïve Bayes classification method which is a learning technique for data mining algorithms that uses probability and statistical methods [4]. This method is used to classify comments given by the community to smartphone brands. The comments given in this application will later be classified into positive, negative, and neutral comments. The purpose of this study was to find out how much positive, negative and neutral comments the community gave to smartphone brands, so that later it would facilitate the smartphone brand in providing policies or development in the future.

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