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Title: :  Document Streams mining by using sequential topic pattern.
PaperId: :  5553
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 3 Issue 3 2017
DUI:    16.0415/IJARIIE-5553
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Prof. P. V. Waje-Kashid S.V.I.T, Chincholi, Nasik, Maharashtra, India
Ms. Poonam H. Kaute S.V.I.T, Chincholi, Nasik, Maharashtra, India
Ms. Pallavi R. Avhad S.V.I.T, Chincholi, Nasik, Maharashtra, India
Ms. Zulfin A. Syyad S.V.I.T, Chincholi, Nasik, Maharashtra, India
Ms. Vaishnavi S. Tajanpure S.V.I.T, Chincholi, Nasik, Maharashtra, India

Abstract

Information Technology
Web mining, sequential patterns, document streams, rare events, pattern-growth, dynamic programming.
Textual documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, in order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can be applied in many real-life scenarios, such as real-time monitoring on abnormal user behaviors. We present a group of algorithms to solve this innovative mining problem through three phases: pre-processing to extract probabilistic topics and identify sessions for different users, generating all the STP candidates with (expected) support values for each user by pattern-growth, and selecting URSTPs by making user-aware rarity analysis on derived STPs. Experiments on both real (Twitter) and synthetic datasets show that our approach can indeed discover special users and interpretable URSTPs effectively and efficiently, which significantly reflect users characteristics.

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IJARIIE Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure . "Document Streams mining by using sequential topic pattern." International Journal Of Advance Research And Innovative Ideas In Education Volume 3 Issue 3 2017 Page 2479-2487
MLA Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure . "Document Streams mining by using sequential topic pattern.." International Journal Of Advance Research And Innovative Ideas In Education 3.3(2017) : 2479-2487.
APA Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , & Ms. Vaishnavi S. Tajanpure . (2017). Document Streams mining by using sequential topic pattern.. International Journal Of Advance Research And Innovative Ideas In Education, 3(3), 2479-2487.
Chicago Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure . "Document Streams mining by using sequential topic pattern.." International Journal Of Advance Research And Innovative Ideas In Education 3, no. 3 (2017) : 2479-2487.
Oxford Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure . 'Document Streams mining by using sequential topic pattern.', International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 3, 2017, p. 2479-2487. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Document_Streams_mining_by_using_sequential_topic_pattern__ijariie5553.pdf (Accessed : ).
Harvard Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure . (2017) 'Document Streams mining by using sequential topic pattern.', International Journal Of Advance Research And Innovative Ideas In Education, 3(3), pp. 2479-2487IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Document_Streams_mining_by_using_sequential_topic_pattern__ijariie5553.pdf (Accessed : )
IEEE Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure , "Document Streams mining by using sequential topic pattern.," International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 3, pp. 2479-2487, May-Jun 2017. [Online]. Available: https://ijariie.com/AdminUploadPdf/Document_Streams_mining_by_using_sequential_topic_pattern__ijariie5553.pdf [Accessed : ].
Turabian Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure . "Document Streams mining by using sequential topic pattern.." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 3 number 3 ().
Vancouver Prof. P. V. Waje-Kashid , Ms. Poonam H. Kaute , Ms. Pallavi R. Avhad , Ms. Zulfin A. Syyad , and Ms. Vaishnavi S. Tajanpure . Document Streams mining by using sequential topic pattern.. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2017 [Cited : ]; 3(3) : 2479-2487. Available from: https://ijariie.com/AdminUploadPdf/Document_Streams_mining_by_using_sequential_topic_pattern__ijariie5553.pdf
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