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A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn project in Java Spring Framework with source code and document.

Java Spring Framework project   Last updated on - July 11, 2018
Kalaiselvi Elumalai
Kalaiselvi Elumalai
php java big data Html Css Javascript Jquery 
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In this page A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn project is a desktop application which is developed in Java Spring Framework platform. This Java Spring Framework project with tutorial and guide for developing a code. A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn is a open source you can Download zip and edit as per you need. If you want more latest Java Spring Framework projects here. This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project. Zip file containing the source code that can be extracted and then imported into Eclipse. Here Project Source code for BE, BTech, MCA, BCA, Engineering, Bs.CS, IT, Software Engineering, Computer Science students and Devloper. Student can submit in college for final year project. This script developed by Kalaiselvi Elumalai. This desktop application 100% working smooth without any bug. It is developed using java,hadoop and Database hdfs. This software code helpful in academic projects and research paper for final year computer science. You can explore great collection of other Java Spring Framework projects.

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About A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn project

project Name

A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn

Project Complexityadvanced
Duration15 Days
project ID2436
Developer NameKalaiselvi Elumalai
Publish DateJuly 11, 2018
project PlatformJava Spring Framework
Programming Languagejava,hadoop
Front End
Back End
IDE ToolEclipse
Database Integrationhdfs
project Typedesktop Application
No of project Download182
project Total View4733
Today Trends1
Current Month Trends4
Last Month Trends44

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Features and function of the A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn project

As market competition intensifies, customer churn management is increasingly becoming an important means of competitive advantage for companies. However, when dealing with big data in the industry, existing churn prediction models cannot work very well. In addition, decision makers are always faced with imprecise operations management. In response to these difficulties, a new clustering algorithm called Semantic Driven Subtractive Clustering Method (SDSCM) is proposed. Experimental results indicate that SDSCM has stronger clustering semantic strength than Subtractive Clustering Method (SCM) and fuzzy c-means (FCM). Then a parallel SDSCM algorithm is implemented through a Hadoop MapReduce framework. In the case study, the proposed parallel SDSCM algorithm enjoys a fast running speed when compared with the other methods. Furthermore, We provide some marketing strategies in accordance with the clustering results, and a simplified marketing activity is simulated to ensure profit maximization.

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