Mining Projected Clusters in High-Dimensional Spaces

In this Mining Projected Clusters in High-Dimensional Spaces data warehousing project we have designed a new distance based clustering algorithm. This algorithm we can reduce & eliminate the previous problems in high dimensional clustering. In this project we have given the total explanation of PCKA Algorithm.

Hardware Requirements
Pentium-4 PC
40 GB hard-disk
512 MB RAM,
Keyboard,
Mouse

Software Requirements
Operating System    Windows 2000 / XP
Software    JAVA,
Java Software Development Kit 1.6
Java Net Beans IDE

The attached document contains the below

Introduction to the project
motivation of the project
problems in the previous system definition of the projects
objective of the project
limitations of the proposed system.
Total literature study of the system
Analysis Phase software requirement specification,
Requirement Phase
Flow Charts & Algorithms.
Designing Phase with UML Diagrams
Project implementation with Source Code
Testing Phase and validation of the project
Screen Shots/ Reports
conclusion and future work of the project.

Mining Projected Clusters in High-Dimensional Spaces

Download Mining Projected Clusters in High-Dimensional Spaces Project Documentation

14 Replies to “Mining Projected Clusters in High-Dimensional Spaces”

  1. I want this project Mining Projected Clusters in High-Dimensional Spaces,. The documentation file which you’ve uploaded does not have full coding of the project. Please provide me this project full coding or java .jar executable file….I would be very grateful..

  2. I want this project Mining Projected Clusters in High-Dimensional Spaces,. The documentation file which you’ve uploaded does not have full coding of the project. Please provide me this project full coding or java .jar executable file….I would be very grateful..

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