A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn Project in Java Spring Framework with Source Code and Report [ download ]

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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. This project can edit using a Eclipse IDE. Following Java Spring Framework project contains all the important features which can be in use for the BE, BTech, MCA, BCA, Engineering, Bs.CS, IT, Software Engineering, Computer Science students and Devloper for their college projects. 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. To download A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn project in Java Spring Framework with source code files, please scroll down to the bottom of this page for the Download Zip file of source code button.

About project

project Name	A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn
Project Complexity	advanced
Duration	15 Days
project ID	2436
Developer Name	Kalaiselvi Elumalai
Publish Date	July 11, 2018
project Platform	Java Spring Framework
Programming Language	For this particular Java Spring Framework project, java,hadoop is required
Front End	HTML, CSS, Bootstrap
Back End	JAVA, MySQL, Oracle, MariaDB
IDE Tool	Eclipse
Database Integration	hdfs
project Type	desktop Application
No of project Download	187
project Total View	5674
Today Trends	557
Current Month Trends	580
Last Month Trends	50

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Class Diagrams

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

We will implement the following feature in the Java Spring Framework 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.

User modules and function of A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn

We will implement the following functionalities in the Java Spring Framework A Big Data Clustering Algorithm For Mitigating The Risk Of Customer Churn Project:

Software requirement to run this project

Java Development Kit (JDK) Version: Java 8 or higher (Java 11 or Java 17 is recommended as of 2024 for LTS support). Spring Framework IDE: Eclipse, IntelliJ IDEA, or VS Code with Java and Spring Boot extensions. Database: MySQL, PostgreSQL or MongoDB. Application Server : Need a web server like Apache Tomcat or Jetty.

Hardware requirement to run this project

Processor (CPU): Minimum: 2-core processor (e.g., Intel Core i3 or equivalent) Recommended: 4-core or better (e.g., Intel Core i5/i7, AMD Ryzen 5/7) RAM (Memory): Minimum: 8 GB of RAM Recommended: 16 GB of RAM (especially if running Docker, multiple services, or heavy IDEs) Hard Disk: Minimum: 50 GB of free disk space (SSD is recommended for faster performance) Recommended: 100 GB or more (for large projects or multiple repositories) Display: Minimum: 1080p resolution (1920x1080) for better productivity, especially if using modern IDEs with multiple panes (for coding, testing, etc.) Recommended: Dual monitors for multitasking (optional but helpful) Operating System: Windows 10/11, macOS, or Linux (Ubuntu, CentOS, etc.) Linux is often preferred for server-side development due to its compatibility with Spring and deployment environments.

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UML & Technical Diagrams Included:
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