scholarly journals Implementing Classification Techniques of Data Mining in Creating Model for Predicting Academic Marketing

2015 ◽  
Vol 7 (7) ◽  
pp. 494-500
Author(s):  
Sheila Abaya ◽  
Bobby Gerardo ◽  
Bartolome Tanguilig
Keyword(s):  
Data Mining ◽  
Classification Techniques ◽  
Academic Marketing

scholarly journals Performance evaluation of different classification techniques using different datasets

2019 ◽  
Vol 9 (5) ◽  
pp. 3584
Author(s):  
Abdulkadir Özdemir ◽  
Uğur Yavuz ◽  
Fares Abdulhafidh Dael
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Multilayer Perceptron ◽  
Data Classification ◽  
Major Effect ◽  
Classification Techniques ◽  
The Difference ◽  
Two Parameters ◽  
Accuracy Performance

<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.</span>

scholarly journals A Tutorial on Hierarchical Classification with Applications in Bioinformatics

2011 ◽  
pp. 114-140
Author(s):  
Alex Freitas ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Protein Function ◽  
Hierarchical Classification ◽  
Classification Problems ◽  
Classification Techniques ◽  
Hierarchical Relationship

In machine learning and data mining, most of the works in classification problems deal with flat classification, where each instance is classified in one of a set of possible classes and there is no hierarchical relationship between the classes. There are, however, more complex classification problems where the classes to be predicted are hierarchically related. This chapter presents a tutorial on the hierarchical classification techniques found in the literature. We also discuss how hierarchical classification techniques have been applied to the area of bioinformatics (particularly the prediction of protein function), where hierarchical classification problems are often found.

scholarly journals An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

2019 ◽  
Vol 9 (4) ◽  
pp. 520-526
Author(s):  
Thakerng Wongsirichot ◽  
Nittida Elz ◽  
Supasit Kajkamhaeng ◽  
Wanchai Nupinit ◽  
Narongrit Sridonthong
Keyword(s):  
Data Mining ◽  
Sleep Stage ◽  
Classification Techniques ◽  
Stage Classification ◽  
Sleep Stage Classification

Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

2020 ◽  
Vol 10 (05) ◽  
pp. 984-987
Author(s):  
A Nagaratnam ◽  
B Deepika ◽  
Shiek Ameer ◽  
T Sharoon ◽  
CH Ajay
Keyword(s):  
Data Mining ◽  
Classification Techniques ◽  
Using Data

Human Talent Forecasting using Data Mining Classification Techniques

2012 ◽  
pp. 261-274
Author(s):  
Hamidah Jantan ◽  
Abdul Ali Hamdan ◽  
Zulaiha Othman
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Talent Management ◽  
Past Experience ◽  
Classification Task ◽  
Close Attention ◽  
Classification Techniques ◽  
Useful Knowledge ◽  
Using Data ◽  
The Right

Talent management is a very crucial task and demands close attention from human resource (HR) professionals. Recently, among the challenges for HR professionals is how to manage organization’s talents, particularly to ensure the right job for the right person at the right time. Some employee’s talent patterns can be identified through existing knowledge in HR databases, which data mining can be applied to handle this issue. The hidden and useful knowledge that exists in databases can be discovered through classification task and has been widely used in many fields. However, this approach has not successfully attracted people in HR especially in talent management. In this regard, the authors attempt to present an overview of talent management problems that can be solved by using this approach. This paper uses that approach for one of the talent management tasks, i.e., predicting potential talent using previous existing knowledge. Future employee’s performances can be predicted based on past experience knowledge discovered from existing databases by using classification techniques. Finally, this study proposes a framework for talent forecasting using the potential Data Mining classification techniques.

Human Talent Forecasting using Data Mining Classification Techniques

2012 ◽  
pp. 486-499
Author(s):  
Hamidah Jantan ◽  
Abdul Razak Hamdan ◽  
Zulaiha Ali Othman
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Talent Management ◽  
Past Experience ◽  
Classification Task ◽  
Close Attention ◽  
Classification Techniques ◽  
Useful Knowledge ◽  
Using Data ◽  
The Right

Talent management is a very crucial task and demands close attention from human resource (HR) professionals. Recently, among the challenges for HR professionals is how to manage organization’s talents, particularly to ensure the right job for the right person at the right time. Some employee’s talent patterns can be identified through existing knowledge in HR databases, which data mining can be applied to handle this issue. The hidden and useful knowledge that exists in databases can be discovered through classification task and has been widely used in many fields. However, this approach has not successfully attracted people in HR especially in talent management. In this regard, the authors attempt to present an overview of talent management problems that can be solved by using this approach. This paper uses that approach for one of the talent management tasks, i.e., predicting potential talent using previous existing knowledge. Future employee’s performances can be predicted based on past experience knowledge discovered from existing databases by using classification techniques. Finally, this study proposes a framework for talent forecasting using the potential Data Mining classification techniques.

Classification Techniques in Data Mining

2017 ◽  
pp. 153-188
Author(s):  
Ali Hosseinzadeh ◽  
S. A. Edalatpanah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Fuzzy Classification ◽  
Classification Techniques ◽  
Learning Machines ◽  
Advantages And Disadvantages ◽  
Behavior Based ◽  
Knowledge Learning

Learning is the ability to improve behavior based on former experiences and observations. Nowadays, mankind continuously attempts to train computers for his purpose, and make them smarter through trainings and experiments. Learning machines are a branch of artificial intelligence with the aim of reaching machines able to extract knowledge (learning) from the environment. Classical, fuzzy classification, as a subcategory of machine learning, has an important role in reaching these goals in this area. In the present chapter, we undertake to elaborate and explain some useful and efficient methods of classical versus fuzzy classification. Moreover, we compare them, investigating their advantages and disadvantages.

Sign in / Sign up

Export Citation Format

Share Document