Applying decision tree data mining for online group buying consumers' behaviour

2008 ◽  
Vol 2 (2) ◽  
pp. 140 ◽  
Author(s):  
Jyh Jian Sheu ◽  
Yao Wen Chang ◽  
Ko Tsung Chu
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Group Buying ◽  
Tree Data ◽  
Online Group

Data-Driven Decision Tree Classification for Product Portfolio Design Optimization

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Engineering Design ◽  
Optimization Techniques ◽  
Product Portfolio ◽  
Performance Expectations ◽  
Data Set ◽  
Tree Data ◽  
Portfolio Design ◽  
Product Concepts

The formulation of a product portfolio requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. A design methodology is presented that significantly enhances the product portfolio design process by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a decision tree data mining technique that generates a set of product concepts that are subsequently validated in the engineering design using multilevel optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: (1) it must satisfy customer price and performance expectations (based on the predictive model) defined here as the feasibility criterion; (2) the feasible set of products/variants validated at the engineering level must generate positive profit that we define as the optimality criterion; (3) the optimal set of products/variants should be a manageable size as defined by the enterprise decision makers and should therefore not exceed the product portfolio limit. The strength of our work is to reveal the tremendous savings in time and resources that exist when decision tree data mining techniques are incorporated into the product portfolio design and selection process. Using data mining tree generation techniques, a customer data set of 40,000 responses with 576 unique attribute combinations (entire set of possible product concepts) is narrowed down to 46 product concepts and then validated through the multilevel engineering design response of feasible products. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, without violating customer product performance expectations.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

scholarly journals Penerapan Decision Tree Data Mining Pada Produksi Kelapa Sawit PT Hindoli Di Sungai Lilin Kabupaten Musi Banyuasin

2020 ◽  
Vol 1 (2) ◽  
pp. 84-99
Author(s):  
Atika Kurnia ◽  
Ahmad Haidar Mirza ◽  
Andri Andri
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Palm Oil ◽  
Oil Palm ◽  
Production Data ◽  
Data Mining Algorithm ◽  
Oil Processing ◽  
Tree Data ◽  
Decision Tree Method ◽  
High Quality Seed

Data mining is an interesting pattern extraction of large amounts of data. PT Hindoli itself has a decision support information system that applies the c4.5 data mining algorithm. Given the large amount of data available, data mining estimates that palm oil production for a month is from production data. As one of the companies engaged in processing palm oil and producing palm oil, palm oil, and high-quality seed oil, which are grown by farmers into materials that can be sold and will be distributed to production data. The method used is the decision tree method to explore data, find hidden relationships between a number of prospective variables, among others, the number of producing oil palm based on the year, production, competition, and price, resulting in data accumulation or data grouping every month. Input with the target variable is expected to help PT Hindoli in monitoring palm oil processing.

scholarly journals An intelligent three-phase spam filtering method based on decision tree data mining

2016 ◽  
Vol 9 (17) ◽  
pp. 4013-4026 ◽  
Author(s):  
Jyh-Jian Sheu ◽  
Yin-Kai Chen ◽  
Ko-Tsung Chu ◽  
Jih-Hsin Tang ◽  
Wei-Pang Yang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Spam Filtering ◽  
Filtering Method ◽  
Three Phase ◽  
Tree Data

scholarly journals Predicting the Individuals’ job satisfaction and determining the factors affecting it using the CHAID Decision Tree Data Mining Algorithm Case Study: the National Opinion Research Center of the United States

2019 ◽  
Vol 4 (3) ◽  
pp. 6-9 ◽  
Author(s):  
Farhad Sheybani
Keyword(s):  
United States ◽  
Data Mining ◽  
Job Satisfaction ◽  
Decision Tree ◽  
The United States ◽  
Research Center ◽  
Data Mining Algorithm ◽  
Factors Affecting ◽  
National Opinion Research ◽  
Tree Data

As the general attitude of the individual about what he does, job satisfaction is the result of individual perceptions from the workplace and the factors and conditions in it; it is also influenced by his personality traits. Meanwhile, investigating job satisfaction is of great importance in advanced societies. The present study aimed to assess job satisfaction in the United States and evaluate the hypothesis of the existence of job dissatisfaction and the factors affecting it in the studied sample. The various social data, related to job satisfaction and collected by the National Opinion Research Center of the United States, are used in this study. The sample consists of different people including male and female samples from nine different states in the United States. For the purpose of this study, the patterns of data were discovered, and factors affecting job satisfaction were identified using the CHAID decision tree data mining method. Finally, it was found that a small percentage of people are dissatisfied with their job.

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