This document outlines a project for outlier detection in high dimensional data. It will analyze techniques for finding outliers by studying projections from datasets, as existing methods make assumptions of low dimensionality that do not apply to very high dimensional data. The system architecture is divided into modules for high dimensional outlier detection, lower dimensional projection, and post processing. Implementation plans include literature review, studying Java, developing the detection system and projections, testing, and documentation. A Gantt chart and cost model are provided.

1. Outlier Detection for High
Dimensional Data
Presented by

2. Outline
• Problem definition
• Literature survey
• System features
• System Architecture
• Analysis Models
• UML diagrams
• System Implementation Plans
• Grantt chart, Cost implementation model

3. Problem definition
The outlier detection technique finds applications in credit card
fraud, network intrusion detection, financial applications and
marketing. This problem typically arises in the context of very high
dimensional data sets. Much of the recent work on find-ing outliers
use methods which make implicit assumptions of relatively low
dimensionality of the data. Thus, we discuss new techniques for
outlier detection which find the outliers by studying the behavior of
projections from the data set.

4. Literature survey
 Many algorithms have been proposed in recent years for out-lier detection, but they
are not methods which are specifically designed in order to deal with the curse of high
dimensionality.
 Two interesting algorithms define outliers by using the full dimensional distances of
the points from one another. This measure is naturally sus-ceptible to the curse of
high dimensionality.
 According to Knorr and Ng, A point p in a data set is an outlier with respect to the
parameters k and A, if no more than k points in the data set are at a distance A or less
from p.
 As pointed out , this method is sensitive to the use of the parameter A which is hard to
figure out a-priori. In addition, when the dimensionality increases, it becomes
in-creasingly difficult to pick.

5. System features
 Hardware & Software Requirements
Hard disk 80 GB
RAM 1GB
Technology Java
Tools Net-beans IDE
Processor Intel Pentium IV or above
Operating System Windows XP

6. System features continued..
Quality Attributes
• Usability : The application seem to user friendly since the GUI is
interactive.
• Maintainability : This application is maintained for long period of time
since it will be implemented under java platform .
• Reusability : The application can be reusable by expanding it to the
new modules
• Portability: The application is purely a portable mobile application since it
can only be operated on android Operating system.

7. System architecture
The system architecture is divided into three modules:
• High dimensional outlier detection
• Lower dimensional projection
• Post processing

8. Use case

9. Activity

10. System Implementation Plan
SR
No
Task Name Start Duration
1 Project topic finalization 10 days
2 Literature 10 days
3 Studying Core java,J2SE 30 days
4 Implementation of High
Dimensional Outlier detection
system
7 days
5 Implementation of Lower
projections
10 days

11. Grant chart & cost implementation
model
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10000
Training System
installation
Studying Core
java
Implementation
of Modules
Testing Documentation
Cost(in RS)
Time(in days)