A Survey on Different Approaches and Techniques of User Web Search Behavior

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-39 Number-5
Year of Publication : 2016
Authors : Ruchika Tripathi, Pankaj Richhariya
  10.14445/22315381/IJETT-V39P241

MLA 

Ruchika Tripathi, Pankaj Richhariya"A Survey on Different Approaches and Techniques of User Web Search Behavior", International Journal of Engineering Trends and Technology (IJETT), V39(5),246-249 September 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Improving the web search engine application and user experience is very essential to figure out user search goals. When user fires a query to search an content, it is desired to be displayed with data that actually he need as a goal. For this purpose it is necessary to figure out the distributions of the user image search goals for that query. The user click session data and click content data from the click-through logs will be examined for understanding the goal distributions. These papers give a brief introduction of various techniques of query segmentation. Here paper has explained different features of web query optimization as well. So whole paper is a complete bunch of user behavior study for optimizing required search.

 References

[1] Manjot Kaur And Navjot Kaur, ― Web Document Clustering Approaches Using K-Mean Algorithm. IJARCSSE, Volume 3, Issue 5, May 2013.
[2] Anoop Jain, Aruna Bajpai And Manish Kumar Rohila, ― Efficient Clustering Technique For Information Retrieval In Data Mining. International Journal Of Emerging Technology And Advanced Engineering (ISSN 2250-2459, Volume 2, Issue 6, June 2012.
[3] S.M. Jagatheesan And V. Thiagarasu, ―Development Of Fuzzy Based Categorical Text Clustering Algorithm For Information Retrieval. International Journal Of Innovative Research In Computer, Vol. 2, Issue 1, January 2014.
[4] S. Anitha Elavarasi And J. Akilandeswari , ― SURVEY ON CLUSTERING ALGORITHM AND SIMILARITY MEASURE FOR CATEGORICAL DATA. ICTACT JOURNAL ON SOFT COMPUTING, JANUARY 2014, VOLUME: 04, ISSUE: 02.
[5] Keole.Ranjit R And Dr.Karde.Pravin.P, ―Information Retrieval From Web Document Using Clustering Techniques. International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 2 Issue 3 March 2013 Page No. 759- 764.
[6] R.Mahalakshmi, V.Lakshmi Praba, ―A Relative Study On Search Results Clustering Algorithms - K-Means, Suffix Tree And LINGO. International Journal Of Engineering And Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-6, August 2013.
[7] Thi Thanh Sang Nguyen, Hai Yan Lu, Web –Page Recommendation Based On Web Usage & Domain Knowledge, Ieee Transaction ,2014.
[8] Poonam B.Lohiya, ―A Survey On Web Search Result Clustering And Engines. International Journal Of Science, Engineering And Technology Research (IJSETR)Volume 2, Issue 2, February 2013 ISSN: 2278 – 7798.
[9] R. Ranjani, S. A. Elavarasi And J. Akilandeswari, “Categorical Data Clustering Using Cosine Based Similarity For Enhancing The Accuracy Of Squeezer Algorithm”, International Journal Of Computer Applications, Vol. 45, No. 20, Pp. 41-45, 2012.
[10] S. Guha, R. Rastogi And K. Shim, “ROCK: A Robust Clustering Algorithm For Categorical Attributes”, Information Systems, Vol. 25, No. 5, Pp. 345 – 366, 2000.
[11] Z. Huang, “Extensions To The K-Means Algorithm For Clustering Large Data Sets With Categorical Values”, Data Mining And Knowledge Discovery, Vol. 2, No. 3, Pp. 283 – 304, 1998.
[12] Z. He, X. Xu, S. Deng And B. Dong, “K- Histograms: An Efficient Clustering Algorithm For Categorical Dataset”, Corr, Vol. Abs/Cs/0509033, 2005.

Keywords
Task Trail, Query Trail, Web Log, Web mining.