Influence of ratio of auxiliary pages on the pre-processing phase of web usage mining
dc.contributor.author | Munk, Michal | |
dc.contributor.author | Benko, Ľubomír | |
dc.contributor.author | Gangur, Mikuláš | |
dc.contributor.author | Turčáni, Milan | |
dc.contributor.other | Ekonomická fakulta | cs |
dc.date.accessioned | 2015-09-02 | |
dc.date.available | 2015-09-02 | |
dc.date.defense | 2015-09-04 | |
dc.date.issued | 2015-09-04 | |
dc.description.abstract | Data mining belongs to the one of the important tools for Business Intelligence. It is a means to increase competitiveness of a company. Web usage mining is engaged in data mining of web server log file and it analyzes the user´s behavior on the web site. The first step of web usage mining process is data pre-processing obtained from a web log file. Data pre-processing is an important part of web usage mining. Discovering patterns of behavior of web visitors depends on the quality of pre-processing phase. Therefore it is important to understand the used methods. This paper summarizes the pre-processing phases and especially the phases of session identification. There are introduced two algorithms for data cleaning and session identification using the reference length method. The main aim of this paper is to compare a calculation of cutoff time and its influence on discovered useful, trivial and inexplicable rules. Cutoff time is an important part of the session identification using the Reference Length method. The influence of ratio of auxiliary pages on the calculation based on a sitemap and subjective estimation was compared. Statistical methods were used to determine the difference between these two approaches. In this paper was examined the portion of found rules based on quantity and quality. The ratio of auxiliary pages has only an impact on quantity of extracted rules in the files with path completion. It has no impact on portion of extracted useful rules, on the other hand, inappropriate estimation of the ratio of auxiliary pages may cause increasing of trivial and inexplicable rules. | en |
dc.format | text | |
dc.format.extent | 144-159 s. | cs |
dc.identifier.doi | 10.15240/tul/001/2015-3-013 | |
dc.identifier.eissn | 2336-5604 | |
dc.identifier.issn | 12123609 | |
dc.identifier.uri | https://dspace.tul.cz/handle/15240/13249 | |
dc.language.iso | en | |
dc.publisher | Technická Univerzita v Liberci | cs |
dc.publisher | Technical university of Liberec, Czech Republic | en |
dc.publisher.abbreviation | TUL | |
dc.relation.isbasedon | ABRAHAM, A. Natural computation for business intelligence from Web usage mining. In: Proceedings of Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. 2005, pp. 3-10. doi: 10.1109/SYNASC.2005.59. | |
dc.relation.isbasedon | Agrawal, R., Imieliński, T., Swami, A. Mining Association Rules Between Sets Of Items In Large Databases. In: SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data. New York: ACM, 1993, pp. 207-216. ISBN 0-89791-592-5. DOI: 10.1145/170036.170072 | |
dc.relation.isbasedon | Agrawal, R., Srikant, R. Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases. San Francisco, CA: Morgan Kaufmann Publishers Inc., 1994. pp. 487-499. | |
dc.relation.isbasedon | Arora, D., Neville, S.W., Li, K.F. Mining WiFi Data for Business Intelligence. In: 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2013. pp. 394-398. doi: 10.1109/3PGCIC.2013.67. | |
dc.relation.isbasedon | AYE, T. Web log cleaning for mining of web usage patterns. In: Computer Research and Development (ICCRD). Vol. 2. IEEE, 2011. pp. 490-494. ISBN 978-1-61284-839-6. DOI: 10.1109/ICCRD.2011.5764181. | |
dc.relation.isbasedon | BERRY, M., LINOFF, G. Data mining techniques for marketing, sales, and customer relationship management. 2nd ed. Indianapolis: Wiley, 2004. 672 p. ISBN 978-0-471-47064-9. | |
dc.relation.isbasedon | Cooley, R., Mobasher, B., Srivastava, J. Data Preparation for Mining World Wide Web Browsing Patterns. Knowledge and Information Systems. 1999, Vol. 1, Iss. 1, pp. 5-32. ISSN 0219-1377. DOI: 10.1007/BF03325089. | |
dc.relation.isbasedon | Electronic statistics textbook. Tulsa, OK: Statsoft, 2010. | |
dc.relation.isbasedon | Frawley, W., Piatetsky‐Shapiro, G., Matheus, C. Knowledge Discovery in Databases: An Overview. AI Magazine. 1992, Vol. 13, Iss. 3, pp. 213‐228. ISSN 0738-4602. DOI: 10.1609/aimag.v13i3.1011. | |
dc.relation.isbasedon | Han, J., Lakshmanan, L., Pei, J. Scalable Frequent-pattern Mining Methods: An Overview. In: Tutorial Notes of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2001. pp. 5.1-5.61. DOI: 10.1145/502786.502792. | |
dc.relation.ispartof | Ekonomie a Management | cs |
dc.relation.ispartof | Economics and Management | en |
dc.relation.isrefereed | true | |
dc.rights | CC BY-NC | |
dc.subject | health care system | en |
dc.subject | in-hospital care | en |
dc.subject | day surgery | en |
dc.subject | Analytical Hierarchy Process | en |
dc.subject | functionality of day surgery | en |
dc.subject.classification | C88 | |
dc.subject.classification | C69 | |
dc.subject.classification | M15 | |
dc.subject.classification | O33 | |
dc.subject.classification | D89 | |
dc.title | Influence of ratio of auxiliary pages on the pre-processing phase of web usage mining | en |
dc.type | Article | en |
local.access | open | |
local.citation.epage | 159 | |
local.citation.spage | 144 | |
local.faculty | Faculty of Economics | |
local.fulltext | yes | |
local.relation.abbreviation | E&M | en |
local.relation.abbreviation | E+M | cs |
local.relation.issue | 3 | |
local.relation.volume | 18 |