scholarly journals Customer Relationship and Sales Management using Data Mining Techniques

2020 ◽  
Vol 2 (3) ◽  
pp. 14-21
Author(s):  
Devaganesh M ◽  
Dilipan B ◽  
Abirami R
Keyword(s):  
Data Mining ◽  
Customer Relationship ◽  
Association Mining ◽  
Data Mining Techniques ◽  
Single Person ◽  
General Store ◽  
Using Data ◽  
Project Data ◽  
Visualization Techniques ◽  
Transactional Data

A general store is a destination for customers to buy everything they require in a single spot. The Manager is responsible for administering and making decisions for developing and running the store. But it is a heavy task for a single person to make valuable decisions that do not affect the business. So, in this project, data mining techniques are applied to the store’s transactional data to help the manager make decisions. We use RFM, Recency Frequency and Monetary segmentation method and classification algorithms to classify the customers into loyal and not-so-loyal customers to make predictions based on the data of the valuable customers. The customers who are loyal to the store are selected and their data is used for further analytics. Association mining technique to obtain the products which are most likely to be bought together and other data mining and visualization techniques to display the valuable knowledge mined from the dataset. Then we cluster the products based on the product’s important keywords and then the customers are further classified by finding the number of products bought from the previously found clusters.

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. 

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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