Decision tree construction for data mining on grid computing

2004 ◽  
Author(s):  
Shu-Tzu Tsai ◽  
Chao-Tung Yang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Grid Computing ◽  
Tree Construction

Clustering through Decision Tree Construction in Geology

2001 ◽  
Vol 6 (2) ◽  
pp. 29-41 ◽  
Author(s):  
J. Juozapavičius ◽  
V. Rapševičius
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Formal Methods ◽  
Hierarchical Clustering ◽  
Vertical Section ◽  
Clustering Algorithms ◽  
Geological Data ◽  
Geological Body ◽  
Learning From Data ◽  
Tree Construction

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.

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

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.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

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.

Data Mining Patters in Grid Computing

2012 ◽  
Vol 2 (3) ◽  
pp. 137-138
Author(s):  
S. Murali S. Murali ◽  
◽  
C. B. Selvalakshmi C. B. Selvalakshmi ◽  
S. Padmadevi S. Padmadevi ◽  
P. N. Karthikayan P. N. Karthikayan
Keyword(s):  
Data Mining ◽  
Grid Computing

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

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