scholarly journals Analysis of Various Decision Tree Algorithms for Classification in Data Mining

2017 ◽  
Vol 163 (8) ◽  
pp. 15-19 ◽  
Author(s):  
Bhumika Gupta ◽  
Aditya Rawat ◽  
Akshay Jain ◽  
Arpit Arora ◽  
Naresh Dhami
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Tree Algorithms

scholarly journals MEMPREDIKSI TINGKAT PEMINAT EKSTRAKURIKULER PADA SISWA SMK ANALISIS KESEHATAN ABDURRAB MENGGUNAKAN ALGORITMA C4.5 (STUDI KASUS: SMK ANALIS KESEHATAN ABDURRAB)

2017 ◽  
Vol 2 (2) ◽  
pp. 220-233
Author(s):  
Luluk Elvitaria
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Foreign Language ◽  
Extracurricular Activities ◽  
School Health ◽  
Vocational School ◽  
Foreign Languages ◽  
Self Development ◽  
Tree Algorithms

Extracurricular activities are additional activities in schools, where through this activity, students can add or explore the skills of students in self-development efforts. One of the extracurricular activities is foreign language extracurricular activities, covering 5 languages ​​namely Arabic, English, German, Mandarin, Japanese. In knowing students' interest in extracurricular activities, a study was conducted on the level of interest in extracurricular activities, namely foreign languages, students at the Vocational School Health Analyst Abdurrab. In predicting the level of interest in foreign languages ​​by the process of data mining using the C45 Algorithm method. C45 algorithm is a group of Decision Tree Algorithms. From this research, the school can find out the extent of interest in foreign languages ​​in students and schools can increase extracurricular activities and students can develop their interest in foreign languages ​​as they wish.

Partition Real Data in Decision Tree Using Statistical Criterion

2013 ◽  
Vol 380-384 ◽  
pp. 1469-1472
Author(s):  
Gui Jun Shan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Real Data ◽  
Statistical Criterion ◽  
Partition Scheme ◽  
C4.5 Decision Tree ◽  
Tree Algorithms ◽  
Partition Method

Partition methods for real data play an extremely important role in decision tree algorithms in data mining and machine learning because the decision tree algorithms require that the values of attributes are discrete. In this paper, we propose a novel partition method for real data in decision tree using statistical criterion. This method constructs a statistical criterion to find accurate merging intervals. In addition, we present a heuristic partition algorithm to achieve a desired partition result with the aim to improve the performance of decision tree algorithms. Empirical experiments on UCI real data show that the new algorithm generates a better partition scheme that improves the classification accuracy of C4.5 decision tree than existing algorithms.

scholarly journals Influence of data mining technology in information analysis of human resource management on macroscopic economic management

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251483
Author(s):  
Ai Zhang
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Decision Tree ◽  
Human Resource Management ◽  
Human Resource ◽  
Classification Accuracy ◽  
Ensemble Classifier ◽  
Economic Management ◽  
Mining Technology ◽  
Tree Algorithms

The purposes are to manage human resource data better and explore the association between Human Resource Management (HRM), data mining, and economic management. An Ensemble Classifier-Decision Tree (EC-DT) algorithm is proposed based on the single decision tree algorithm to analyze HRM data. The involved single decision tree algorithms include C4.5, Random Tree, J48, and SimpleCart. Then, an HRM system is established based on the designed algorithm, and the evaluation management and talent recommendation modules are tested. Finally, the designed algorithm is compared and tested. Experimental results suggest that C4.5 provides the highest classification accuracy among the single decision tree algorithms, reaching 76.69%; in contrast, the designed EC-DT algorithm can provide a classification accuracy of 79.97%. The proposed EC-DT algorithm is compared with the Content-based Recommendation Method (CRM) and the Collaborative Filtering Recommendation Method (CFRM), revealing that its Data Mining Recommendation Method (DMRM) can provide the highest accuracy and recall, reaching 35.2% and 41.6%, respectively. Therefore, the data mining-based HRM system can promote and guide enterprises to develop according to quantitative evaluation results. The above results can provide a reference for studying HRM systems based on data mining technology.

scholarly journals Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison

2020 ◽  
Vol 4 (2) ◽  
pp. 57-66
Author(s):  
Ardytha Luthfiarta ◽  
Junta Zeniarja ◽  
Edi Faisal ◽  
Wibowo Wicaksono
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Credit Card ◽  
Banking System ◽  
Decision Making Process ◽  
Data Mining Techniques ◽  
Related Information ◽  
Customer Information ◽  
Tree Algorithms

Banking system collect enormous amounts of data every day. This data can be in the form of customer information,  transaction  details,  risk profiles,   credit   card   details,   limits   and   collateral    details, compliance  Anti Money Laundering (AML) related information, trade  finance  data,  SWIFT  and  telex  messages. In addition,  Thousands  of decision  are  made in Banking system. For example, banks everyday creates credit decisions,  relationship  start  up,  investment   decisions, AML  and  Illegal  financing  related decision.  To create this decision, comprehensive review on various  reports  and drills  down  tools  provided  by the banking systems is needed.  However, this is a manual process which  is  error  prone  and  time  consuming  due  to  large volume of transactional  and historical  data available. Hence, automatic knowledge mining is needed to ease the decision making process.  This research focuses on data mining techniques to handle the mentioned problem. The technique will focus on classification method using Decision Tree algorithms.  This research provides an overview of the data mining techniques and   procedures will be performed.   It also provides   an insight   into how these techniques can be used in deposit subscription  in banking system to make a decision making process easier and more productive. Keywords - Telemarketing, bank deposit, decision tree, classification, data mining, entropy.

FULL-VIEW: A VISUAL DATA-MINING ENVIRONMENT

2002 ◽  
Vol 02 (01) ◽  
pp. 127-143 ◽  
Author(s):  
FRANÇOIS POULET
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Domain Knowledge ◽  
High Performance ◽  
Visual Data Mining ◽  
Data Mining Algorithms ◽  
Research Areas ◽  
Graphical Environment ◽  
Bulletin Boards ◽  
Tree Algorithms

This paper presents a 3D user-centered interactive graphical environment dedicated to data mining. The aims of this environment are to involve the user in the data mining process (to use domain knowledge during the process), to improve comprehensibility (of both the data and the results of data mining algorithms), to improve interactivity and to use algorithms from various research areas: statistics, data analysis, visualization and machine learning. The environment is made of a set of bulletin boards where the graphical tools will be mapped; bulletin boards are predefined or can be user-defined. Several different visualization tools might be used in a single display, these tools are linked together to improve data comprehensibility. The tools available in the environment are both graphical and non-graphical tools, they can be used alone or in a cooperative way. One of these tools is more detailed: CIAD, a new graphical interactive decision tree construction algorithm that allows bivariate splits and so gives smaller trees (improving result comprehensibility). Its results are compared to existing decision tree algorithms. This environment can be used on any personal computer (it is based on open-source software and so, is platform independent) as well as on high performance graphical systems like reality centers.

scholarly journals Machine Learning by Data Mining REPTree and M5P for Predicating Novel Information for PM10

2020 ◽  
pp. 40-48
Author(s):  
Yas Alsultanny
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Meteorological Parameters ◽  
Pm10 Concentration ◽  
Divide And Conquer ◽  
Time Processing ◽  
Elapsed Time ◽  
Tree Algorithms

We examined data mining as a technique to extract knowledge from database to predicate PM10 concentration related to meteorological parameters. The purpose of this paper is to compare between the two types of machine learning by data mining decision tree algorithms Reduced Error Pruning Tree (REPTree) and divide and conquer M5P to predicate Particular Matter 10 (PM10) concentration depending on meteorological parameters. The results of the analysis showed M5P tree gave higher correlation compared with REPTree, moreover lower errors, and higher number of rules, the elapsed time for processing REPTree is less than the time processing of M5P. Both of these trees proved that humidity absorbed PM10. The paper recommends REPTree and M5P for predicting PM10 and other pollution gases.

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