scholarly journals New Insight into Customer Value Analysis using Data Mining Techniques

2017 ◽  
Vol 176 (3) ◽  
pp. 27-38
Author(s):  
Nesma Taher ◽  
Shaimaa Salama ◽  
Doaa ElZanfaly
Keyword(s):  
Data Mining ◽  
Customer Value ◽  
Value Analysis ◽  
Data Mining Techniques ◽  
Customer Value Analysis ◽  
Using Data ◽  
Insight Into

Customer Value Analysis in Banks Using Data Mining and Fuzzy Analytic Hierarchy Processes

2018 ◽  
Vol 17 (03) ◽  
pp. 819-840 ◽  
Author(s):  
Alireza Bashiri Mosavi ◽  
Amir Afsar
Keyword(s):  
Data Mining ◽  
Customer Value ◽  
Customer Relationship ◽  
Support Vector ◽  
Analytic Hierarchy ◽  
Value Analysis ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Starting Point ◽  
Using Data

The customer value is a starting point for customer relationship management to realize and evaluate the value of the customers for every organization. Value is the base of all of the marketing activities because all parties expect to get value in their transactions. This study focusses on the Tejarat Bank branches in Iran and systematically integrates several data mining techniques and management issues in order to analyze customer value. First, we applied the fuzzy analytic hierarchy process for weighing attributes and then imported the demographic, frequency, money and trust attributes to the [Formula: see text]-means. Using the proposed scoring model, we created the customer value pyramid. Finally, in order to analyze the obtained pyramid classes and perform the learning process from the data, we utilized a decision tree, support vector machine, random forest classification techniques, along with six chosen characteristics and introduced the most appropriate model according to the applied attributes. Including learning patterns for classifying new customers, with respect to the importance of defined factors, would be useful in this case. According to results, we achieved a model attribute with the best characteristics in accuracy, precision, recall, [Formula: see text]-measure and class error measures.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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