To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

1. GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com
Mining Probabilistically Frequent Sequential Patterns in
Large Uncertain Databases
Abstract
Data uncertainty is inherent in many real-world applications such as environmental surveillance
and mobile tracking. Mining sequential patterns from inaccurate data, such as those data arising
from sensor readings and GPS trajectories, is important for discovering hidden knowledge in
such applications. In this paper, we propose to measure pattern frequentness based on the
possible world semantics. We establish two uncertain sequence data models abstracted from
many real-life applications involving uncertain sequence data, and formulate the problem of
mining probabilistically frequent sequential patterns (or p-FSPs) from data that conform to our
models. However, the number of possible worlds is extremely large, which makes the mining
prohibitively expensive. Inspired by the famous PrefixSpan algorithm, we develop two new
algorithms, collectively called U-PrefixSpan, for p-FSP mining. U-PrefixSpan effectively avoids
the problem of “possible worlds explosion”, and when combined with our four pruning and
validating methods, achieves even better performance. We also propose a fast validating method
to further speed up our U-PrefixSpan algorithm. The efficiency and effectiveness of U-PrefixSpan
are verified through extensive experiments on both real and synthetic datasets.
Existing system
Data uncertainty is inherent in many real-world applications such as environmental surveillance
and mobile tracking. Mining sequential patterns from inaccurate data, such as those data arising

2. from sensor readings and GPS trajectories, is important for discovering hidden knowledge in
such applications.
Proposed system
In this paper, we propose to measure pattern frequentness based on the possible world semantics.
We establish two uncertain sequence data models abstracted from many real-life applications
involving uncertain sequence data, and formulate the problem of mining probabilistically
frequent sequential patterns (or p-FSPs) from data that conform to our models. However, the
number of possible worlds is extremely large, which makes the mining prohibitively expensive.
Inspired by the famous PrefixSpan algorithm, we develop two new algorithms, collectively
called U-PrefixSpan, for p-FSP mining. U-PrefixSpan effectively avoids the problem of
“possible worlds explosion”, and when combined with our four pruning and validating methods,
achieves even better performance. We also propose a fast validating method to further speed up
our U-PrefixSpan algorithm. The efficiency and effectiveness of U-PrefixSpan are verified
through extensive experiments on both real and synthetic datasets.
System Configuration:-
Hardware Configuration:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA

3. Software Configuration:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.