Abstract
Mining outliers from large datasets is like finding needles in a haystack. Even more challenging is sifting through the dynamic, unstructured, and ever-growing web data for outliers. This paper presents HyCOQ, which is a hybrid algorithm that draws from the power of n-gram-based and word-based systems. Experimental results obtained using embedded motifs without a dictionary show significant improvement over using a domain dictionary irrespective of the type of data used (words, n-grams, or hybrid). Also, there is remarkable improvement in recall with hybrid documents compared to using raw words and n-grams without a domain dictionary.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agyemang, M., Barker, K., Alhajj, R.: Framework for Mining Web Content Outliers. In: Proc. of ACM SAC, Cyprus, pp. 590–594 (2004)
Agyemang, M., Barker, K., Alhajj, R.: Mining Web Content Outliers using Structure Oriented Weighting Techniques and N-grams. In: Proc. of ACM SAC, New Mexico (2005)
Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: Identifying Outliers in Large Dataset. In: Proc. of ACM SIGMOD, Dallas, TX, pp. 93–104 (2000)
Barnett, V., Lewis, T.: Outliers in Statistical Data. John Wiley, Chichester (1994)
Cavnar, B.W., Trenkle, M.J.: N-Gram-Based Text Categorization. In: Proc. of SDAIR (1994)
Chakrabarti, S., et al.: Mining the Link Structure of the Would Wide Web. IEEE Computer 32(8), 60–67 (1999)
Chakrabarti, S.: Data Mining for Hypertext- A Tutorial Survey. ACM SIGKDD Explorations 1(2), 1–11 (2000)
Cooley, R., Mobasher, B., Srivastava, J.: Information and Pattern Discovery on the World Wide Web. In: SIGKDD Explorations, vol. 1(2) (2000)
Damashek, M.: Gauging Similarity with N-Grams: Language Independent Categorization of Text. Science 267, 843–848 (1995)
Etzioni, O.: The World Wide Web: Quagmire or Gold Mine. Communication of the ACM 39(11), 65–68 (1996)
Jin, W., Tung, A.K.-H., Han, J.: Mining Top-n Local Outliers in Large Databases. In: Proc. of ACM SIGKDD, San Francisco, CA, pp. 293–298 (2001)
Johnson, T., Kwok, I., Ng, R.: Fast Computation of 2-D depth Contours. In: Proc. of ACM SIGKDD, pp. 224–228 (1998)
Knorr, E.M., Ng, R.T.: A Unified Notion of Outliers: Properties and Computation. In: Proc. of KDD, pp. 219–222 (1997)
Knorr, E.M., Ng, R.T.: Algorithms for Mining Distance-Based Outliers in Large Dataset. In: Proc. of VLDB, New York, pp. 392–403 (1998)
Liu, B., Ma, Y., Yu, P.S.: Discovering Unexpected Information from Your Competitors’ Web Sites. In: Proc. of ACM SIGKDD, San Francisco, CA, pp. 144–153 (2001)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient Algorithms for Mining Outliers from Large Data Set. In: Proc. of ACM SIGMOD, pp. 427–438 (2000)
Salton, G., Buckley, C.: Term-Weighting Approaches in automatic Text Retrieval. Information Processing and Management 24(5), 513–523 (1988)
http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data , (December 2004)
http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words , (December 2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Agyemang, M., Barker, K., Alhajj, R. (2005). Hybrid Approach to Web Content Outlier Mining Without Query Vector. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_28
Download citation
DOI: https://doi.org/10.1007/11546849_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
eBook Packages: Computer ScienceComputer Science (R0)