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A Framework for Periodic Outlier Pattern Detection in Time-S

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. 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 Contact Us 91 98406 78906, 91 90037 18877 kaashiv.info@gmail.com www.kaashivinfotech.com Shivanantha Building (Second building to Ayyappan Temple), X41, 5th Floor, 2nd avenue, Anna Nagar,Chennai-40

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A Framework for Periodic Outlier Pattern Detection in Time-S

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  1. Tri-patternisation on generic visualized time series data IEEE TRANSACTIONS ON CYBERNETICS, VOL. 44, NO. 5, MAY 2014A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences

  2. A Software /Manufacturing Research Company Run By Microsoft Most Valuable Professional VenkatesanPrabu .J MANAGING DIRECTOR Microsoft Web Developer Advisory Council team member and a well known Microsoft Most Valuable Professional (MVP) for the year 2008, 2009, 2010,2011,2012,2013 ,2014. LakshmiNarayanan.J GENERAL MANAGER BlackBerry Server Admin. Oracle 10g SQL Expert. Arunachalam.J Electronic Architect Human Resourse Manager

  3. 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. • 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.

  4. 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.

  5. 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. • 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.

  6. 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

  7. Architecture Diagram

  8. Records Breaks Asia Book Of Records Tamil Nadu Of Records India Of Records MVP Awards World Record

  9. Services: A Software /Manufacturing Research Company Run By Microsoft Most Valuable Professional Inplant Training. Internship. Workshop’s. Final Year Project’s. Industrial Visit. Contact Us: +91 98406 78906,+91 90037 18877 kaashiv.info@gmail.com www.kaashivinfotech.com Shivanantha Building (Second building to Ayyappan Temple),X41, 5th Floor, 2nd avenue,Anna Nagar,Chennai-40.

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