Abstract
The advent of web had resulted in a plethora of information and data. However, its volume heterogeneity and unstructured organization makes information retrieval difficult. To the existing practice where website categorization is largely based on style rather than text, addition of an extra dimension in form of genre is expected to significantly improve the search outcome. Keeping this in view, we attempt to build a novel classification model to categorize websites into genres using thresholds of the web metrics. Statistical measures of central tendency are assumed to render a value that distinguish websites from a sample space containing News, Travel and Tourism, Entertainment and Social media. Through the statistical analysis of the data we find that the data distribution of all metrics which constitute the website properties are highly skewed. Hence, conventional analysis based on normal distribution statistics fails to apply. Adopting to a systematic empirical approach, we find that the classification performance measure identified through the Area Under the Curve is maximized around a threshold value which is twice the value of the “median-absolute-deviation” of the web metrics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chetry, R.: Web genre classification using feature selection and semi-supervised learning (2011)
Gatto, M.: Web as Corpus: Theory and Practice. Bloomsbury Academic, London (2014)
Stein, B., Zu Eissen, S.M., Lipka, N.: Web genre analysis: use cases, retrieval models, and implementation issues. In: Genres on the Web, pp. 167–189. Springer, Dordrecht (2010)
Ponzanelli, L., Mocci, A., Lanza, M.: Summarizing complex development artifacts by mining heterogeneous data. In: Proceedings of the 12th Working Conference on Mining Software Repositories, pp. 401–405. IEEE Press, New York (2015)
Wu, L., Du, L., Liu, B., Xu, G., Ge, Y., Fu, Y., Li, J., Zhou, Y., Xiong, H.: Heterogeneous metric learning with content-based regularization for software artifact retrieval. In: IEEE International Conference on Data Mining (ICDM), pp. 610–619. IEEE, New York (2014)
Shepherd, M., Watters, C.: The functionality attribute of cybergenres. In: Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, HICSS-32, p. 9. IEEE, New York (1999)
Shepherd, M., Watters, C.: Identifying web genre: hitting a moving target. In: Proceedings of the WWW Conference. Workshop on Measuring Web Search Effectiveness: The User Perspective, vol. 18, New York (2004)
Rosso, M.A.: Using genre to improve web search. Doctoral dissertation, University of North Carolina, Chapel Hill (2005)
Williams, K.C.M.: Reproduced and emergent genres of communication on the World Wide Web. Inf. Soc. 16, 201–215 (2000)
Santini, M.: Characterizing genres of web pages: genre hybridism and individualization. In: HICSS 40th Annual Hawaii International Conference on System Sciences, pp. 71–71. IEEE, New York (2007)
Roussinov, D., Crowston, K., Nilan, M., Kwasnik, B., Cai, J., Liu, X.: Genre based navigation on the web. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences, p. 10. IEEE, New York (2001)
Crowston, K., Kwasnik, B.H.: A framework for creating a facetted classification for genres: addressing issues of multidimensionality. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences, p. 9. IEEE, New York (2004)
Copestake, A.: Errors in wikis. In: Proceedings of the Workshop on NEW TEXT Wikis and Blogs and Other Dynamic Text Sources (2006)
Mehler, A.: Text linkage in the wiki medium: a comparative study. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy, April 3–7, 2006 (EACL 2006): Workshop on New Text—Wikis and blogs and other dynamic text sources, pp. 1–8 (2006)
Lindemann, C., Littig, L.: Classification of web sites at super-genre level. In: Genres on the Web, pp. 211–235. Springer Netherlands (2010)
Erni, K., Lewerentz, C.: Applying design-metrics to object-oriented frameworks. In: Proceedings of the 3rd International on Software Metrics Symposium, pp. 64–74. IEEE, New York (1996)
French, V.: Establishing software metric thresholds. In: Proceedings of the 9th International Workshop on Software Measurement (1999)
de Siqueira, G.O., de Assis, G.T., Almeida Ferreira, A., Mangaravite, V., Cardeal P´adua, F.L.: Strategies for automatic determination of similarity threshold for genre-aware focused crawling processes. In: IADIS International Journal on WWW/Internet, vol. 15 (2017)
Shatnawi, R., Li, W., Swain, J., Newman, T.: Finding software metrics threshold values using ROC curves. J. Softw. Maint. Evol.: Res. Pract. 22, 1–16 (2010)
Shatnawi, R.: A quantitative investigation of the acceptable risk levels of OO metrics in open-source systems. IEEE Trans. Softw. Eng. 36, 216–225 (2010)
Bender, R.: Quantitative risk assessment in epidemiological studies investigating threshold effects. Biom. J.: J. Math. Methods Biosci. 41, 305–319 (1999)
Malhotra, R., Bansal, A.J.: Fault prediction considering threshold effects of object-oriented metrics. Expert. Syst. 32, 203–219 (2015)
Shatnawi, R.: Deriving metrics thresholds using log transformation. J. Softw.: Evol. Process. 27, 95–113 (2015)
Alves, T.L., Ypma, C., Visser, J.: Deriving metric thresholds from benchmark data. In: IEEE International Conference on Software Maintenance (ICSM), pp. 1–10. (2010)
Ferreira, K.A., Bigonha, M.A., Bigonha, R.S., Mendes, L.F., Almeida, H.C.: Identifying thresholds for object-oriented software metrics. J. Syst. Softw. 85, 244–257 (2012)
Hussain, S., Keung, J., Khan, A.A., Bennin, K.E.: Detection of fault-prone classes using logistic regression based object-oriented metrics thresholds. In: IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 93–100 (2016)
Malhotra, R., Sharma, A.: A web metric collection and reporting system. In: Proceedings of the Third International Symposium on Women in Computing and Informatics, pp. 661–667. ACM, New York (2015)
Malhotra, R., Sharma, A.: Quantitative evaluation of web metrics for automatic genre classification of web pages. Int. J. Syst. Assur. Eng. Manag. 8, 1567–1579 (2017)
Frman, G., Cohen, I.: Learning from little: comparison of classifiers given little training. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 161–172. Springer, Berlin (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Malhotra, R., Sharma, A. (2019). An Empirical Study to Classify Website Using Thresholds from Data Characteristics. In: Hu, YC., Tiwari, S., Mishra, K., Trivedi, M. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 904. Springer, Singapore. https://doi.org/10.1007/978-981-13-5934-7_39
Download citation
DOI: https://doi.org/10.1007/978-981-13-5934-7_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5933-0
Online ISBN: 978-981-13-5934-7
eBook Packages: EngineeringEngineering (R0)