Periodic pattern detection in time-ordered sequences is an important data mining task, which discovers in the time series all patterns that exhibit temporal regularities. Periodic pattern mining has a large number of applications in real life; it helps understanding the regular trend of the data along time, and enables the forecast and prediction of future events. An interesting related and vital problem that has not received enough attention is to discover outlier periodic patterns in a time series. Outlier patterns are defined as those which are different from the rest of the patterns; outliers are not noise. While noise does not belong to the data and it Is mostly eliminated by preprocessing, outliers are actual instances in the data but have exceptional characteristics compared with the majority of the other instances. Outliers are unusual patterns that rarely occur, and, thus, have lesser support (frequency of appearance) in the data. Outlier patterns may hint toward discrepancy in the data such as fraudulent transactions, network intrusion, and change in customer behavior, recession in the economy, epidemic and disease Biomarkers, severe weather conditions like tornados, etc. We argue that detecting the periodicity of outlier patterns might be more important in many sequences than the periodicity of regular, more frequent patterns. In this paper, we present a robust and time efficient suffix tree-based algorithm capable of detecting the periodicity of outlier patterns in a time series by giving more significance to less frequent yet periodic patterns. Several experiments have been conducted using both real and synthetic data; all aspects of the proposed approach are compared with the existing algorithm Info Miner; the reported results demonstrate the effectiveness and applicability of the proposed approach. http://kaashivinfotech.com/ http://inplanttrainingchennai.com/ http://inplanttraining-in-chennai.com/ http://internshipinchennai.in/ http://inplant-training.org/ http://kernelmind.com/ http://inplanttraining-in-chennai.com/ http://inplanttrainingchennai.com/

1. IEEE TRANSACTIONS ON CYBERNETICS, VOL. 44, NO. 5, MAY
2014
A Framework for Periodic Outlier Pattern Detection in Time-
Series Sequences

2. Abstract
 Periodic pattern detection in time-ordered sequences is an important data
mining task, which discovers in the time series all patterns that exhibit
temporal regularities. Periodic pattern mining has a large number of
applications in real life; it helps understanding the regular trend of the data
along time, and enables the forecast and prediction of future events. An
interesting related and vital problem that has not received enough attention
is to discover outlier periodic patterns in a time series. Outlier patterns are
defined as those which are different from the rest of the patterns; outliers are
not noise. While noise does not belong to the data and it
 Is mostly eliminated by preprocessing, outliers are actual instances in the
data but have exceptional characteristics compared with the majority of the
other instances. Outliers are unusual patterns that rarely occur, and, thus,
have lesser support (frequency of appearance) in the data. Outlier patterns
may hint toward discrepancy in the data such as fraudulent transactions,
network intrusion, and change in customer behavior, recession in the
economy, epidemic and disease
 Biomarkers, severe weather conditions like tornados, etc. We argue that
detecting the periodicity of outlier patterns might be more important in
many sequences than the periodicity of regular, more frequent patterns. In
this paper, we present a robust and time
 Index Terms-Outlier periodic patterns, performance, periodicity detection,
suffix tree, surprising patterns, surprising periodicity, time series, and
unusual periods.

3. Proposed System
 In general, privacy risks square measure essential quandary
related to confidential information of each structure
therefore care needs to be taken so as to preserve
confidential information.
 Visualization of knowledge by graphical, applied statistical
and hierarchal illustration square measure evolved to
represent the info as within the existing system.
 The usage of vary values, interpretation of disturbance
known as noise are added erected with range values of the
time series data to be visualized.
 The algorithmic rule known as information fly is to be
accustomed add rip-roaring information with vary values of
your time series, this successively doesn't pertain the end-user
to predict the particular statistic from the
visualization.
 Hence this method overcomes the disadvantage raised
within the existing system.

4. Existing System
 In the existing system, the Concept of privacy protective has been
developed in order to preserve information with none clue even
once Multiple generalized techniques has been introduced to
filter the info and cluster that square measure supported the
statistic grouping formula it's unconcealed in varied fields.
 Hence the strategy of approximation was developed before
reveling the info.
 Visualization of Data by suggests that of Graphical, Statistical
and Hierarchal representation are evolved within the system so
as to preserve the Data.
 Accurate vary for the information’s to be preserved has been
erected to envision the statistic data.
 The prediction is that visualization of the time series data
according to the range values does not make out the end user to
frame a clear idea about the data. But it makes out the end user to
predict using the range values.

5. System Requirements
 Hardware Requirements
Processor : Core 2 duo
Speed : 2.2GHZ
RAM : 2GB
Hard Disk : 160GB
 Software Requirements:
Platform : DOTNET (VS2010)Dot net
framework 4.0
Database : SQL Server 2008 R2

6. Architecture Diagram