Decision Tree Construction for Data Mining on Grid Computing Environments

The article presents a tool to analyze the application of efficient algorithms of data mining, namely hierarchical clustering algorithms to be used in the analysis of geological data. It introduces a description of hierarchical clustering principles and methods for learning dependencies from geological data. The authors are using statistical formulation of algorithms to represent the most natural framework for learning from data. The geological data come from mining holes, and describe the structure of sedimental layers of vertical section of geological body. The analysis of such data is intended to give a basis for uniform description of lithological characteristics, and for the identification of them via formal methods.

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Distributed data mining in grid computing environments

Future Generation Computer Systems ◽

10.1016/j.future.2006.04.010 ◽

2007 ◽

Vol 23 (1) ◽

pp. 84-91 ◽

Cited By ~ 22

Author(s):

Ping Luo ◽

Kevin Lü ◽

Zhongzhi Shi ◽

Qing He

Keyword(s):

Data Mining ◽

Grid Computing ◽

Distributed Data Mining ◽

Distributed Data ◽

Computing Environments

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ВИКОРИСТАННЯ ТЕХНОЛОГІЇ DATA MINING ІЗ МЕТОЮ ДИФЕРЕНЦІАЛЬНОЇ ДІАГНОСТИКИ КОМОРБІДНИХ СТАНІВ ХРОНІЧНОГО ПАНКРЕАТИТУ Й АСКАРИДОЗУ НА ПІДСТАВІ ДАНИХ КЛІНІЧНОЇ СИМПТОМАТИКИ Й УЛЬТРАЗВУКОВИХ ДОСЛІДЖЕНЬ

Medical Informatics and Engineering ◽

10.11603/mie.1996-1960.2017.4.8447 ◽

2018 ◽

Author(s):

V. P. Martsenyuk ◽

L. S. Babinets ◽

Yu. V. Dronyak

Keyword(s):

Data Mining ◽

Chronic Pancreatitis ◽

Decision Tree ◽

Clinical Symptomatology ◽

Tree Construction

For diagnostics of chronic pancreatitis and ascaridosis comorbidity methodology of decision tree construction based on C5.0 algorithm is used. Data of both clinical symptomatology and ultrasonography can be applied. For each of types of researches and also for their totality a separate decision tree is built. The error of algorithm is investigated.

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Challenges and Cloud Computing Environments Towards Big Data

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207277 ◽

2014 ◽

pp. 203-208

Author(s):

Kiran Kumar S V N Madupu

Keyword(s):

Data Mining ◽

Cloud Computing ◽

Big Data ◽

Technology Development ◽

Computing Environments ◽

Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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