Data Stream Mining: A Review on Windowing Approach

1
Mr. Pramod S.
Mr. Pramod S.
2
Mr. Pramod S. and O.P Vyas
Mr. Pramod S. and O.P Vyas
1 Christian College of Engineering and Technology and IIIT, Allahabad

Send Message

To: Author

GJCST Volume 12 Issue C11

Article Fingerprint

ReserarchID

CSTSDE55KRF

Data Stream Mining: A Review on Windowing Approach Banner
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

In the data stream model the data arrive at high speed so that the algorithms used for mining the data streams must process them in very strict constraints of space and time. This raises new issues that need to be considered when developing association rule mining algorithms for data streams. So it is important to study the existing stream mining algorithms to open up the challenges and the research scope for the new researchers. In this paper we are discussing different type windowing techniques and the important algorithms available in this mining process.

26 Cites in Articles

References

  1. Fernando Crespo,Richard Weber (2005). A methodology for dynamic data mining based on fuzzy clustering.
  2. David Hand,Heikki Mannila,Padhraic Smyth (2001). Principles of Data Mining.
  3. Maria Halkidi Quality assessment and Uncertainty Handling in Data Mining Process.
  4. B Liu,W Hsu,Y Ma (1998). Integrating Classification and Association Rule Mining.
  5. J Chang,W Lee (2003). Finding Recent Frequent Itemsets Adaptively over Online Data Streams.
  6. C Giannella,J Han,J Pei,X Yan,P Yu (2004). Mining Frequent Patterns in Data Streams at Multiple Time Granularities.
  7. D Lee,W Lee (2005). Finding Maximal Frequent Itemsets over Online Data Streams Adaptively.
  8. Yun Chi,Haixun Wang,Philip Yu,Richard Muntz (2006). Catch the moment: maintaining closed frequent itemsets over a data stream sliding window.
  9. Nicolas Pasquier,Yves Bastide,Rafik Taouil,Lotfi Lakhal (1999). Discovering Frequent Closed Itemsets for Association Rules.
  10. Mohammed Zaki (2000). Generating non-redundant association rules.
  11. M Zaki,C Hsiao (2002). CHARM: An Efficient Algorithm for Closed Itemset Mining.
  12. Jianyong Wang,Jiawei Han,Jian Pei (2003). CLOSET+.
  13. K Gouda,M Zaki (2001). Efficiently mining maximal frequent itemsets.
  14. Toon Calders,Bart Goethals (2002). Mining All Non-derivable Frequent Itemsets.
  15. Jean-François Boulicaut,Artur Bykowski,Christophe Rigotti (2003). Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries.
  16. Jian Pei,Guozhu Dong,Wei Zou,Jiawei Han (2004). Mining Condensed Frequent-Pattern Bases.
  17. D Xin,J Han,X Yan,H Cheng (2005). Mining Compressed Frequent-Pattern Sets.
  18. F Bonchi,C Lucchese (2005). On Condensed Representations of Constrained Frequent Patterns.
  19. James Cheng,Yiping Ke,Wilfred Ng (2006). \delta-Tolerance Closed Frequent Itemsets.
  20. R Jin,G (2005). An Algorithm for In-Core Frequent Itemset Mining on Streaming Data.
  21. D Ltc Bruce,J Caulkins,M Lee,Wang (2005). A Dynamic Data Mining Technique for Intrusion Detection Systems.
  22. Y Chi,H Wang,P Yu,R Muntz (2006). Catch the Moment: Maintaining Closed Frequent Itemsets over a Data Stream Sliding Window.
  23. Heikki Mannila,Hannu Toivonen,A Inkeri Verkamo (1997). Discovery of Frequent Episodes in Event Sequences.
  24. S Graham Cormode,Muthukrishnan (2005). What's Hot and What's Not: Tracking Most Frequent Items Dynamically.
  25. Cheqing Jin,Weining Qian,Chaofeng Sha,Jeffrey Yu,Aoying Zhou (2003). Dynamically maintaining frequent items over a data stream.
  26. Hua-Fu Li,Suh-Yin,Man-Kwan Lee,Shan (2004). An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Mr. Pramod S.. 2012. \u201cData Stream Mining: A Review on Windowing Approach\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 12 (GJCST Volume 12 Issue C11): .

Download Citation

Issue Cover
GJCST Volume 12 Issue C11
Pg. 27- 30
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Classification
Not Found
Version of record

v1.2

Issue date

July 17, 2012

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices
Total Views: 10353
Total Downloads: 2748
2026 Trends
Research Identity (RIN)
Related Research

Published Article

In the data stream model the data arrive at high speed so that the algorithms used for mining the data streams must process them in very strict constraints of space and time. This raises new issues that need to be considered when developing association rule mining algorithms for data streams. So it is important to study the existing stream mining algorithms to open up the challenges and the research scope for the new researchers. In this paper we are discussing different type windowing techniques and the important algorithms available in this mining process.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]
×

This Page is Under Development

We are currently updating this article page for a better experience.

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Data Stream Mining: A Review on Windowing Approach

Mr. Pramod S. and O.P Vyas
Mr. Pramod S. and O.P Vyas

Research Journals