A survey of fuzzy web mining

Advanced Review

Tzung‐Pei Hong, Chun‐Wei Lin

Published Online: Apr 18 2013

DOI: 10.1002/widm.1091

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

Abstract The Internet has become an unlimited resource of knowledge, and is thus widely used in many applications. Web mining plays an important role in discovering such knowledge. This mining can be roughly divided into three categories, including Web usage mining, Web content mining, and Web structure mining. Data and knowledge on the Web may, however, consist of imprecise, incomplete, and uncertain data. Because fuzzy‐set theory is often used to handle such data, several fuzzy Web‐mining techniques have been proposed to reveal fuzzy and linguistic knowledge. This paper reviews these techniques according to the three Web‐mining categories above—fuzzy Web usage mining, fuzzy Web content mining, and fuzzy Web structure mining. Some representative approaches in each category are introduced and compared. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Web Mining Technologies > Computational Intelligence

References

Etzioni, O.The world‐wide web: Quagmire or gold mine?Commun ACM1996,)39):65–68.
Cooley, R,Mobasher, B,Srivastava, J.Web mining: information and pattern discovery on the world wide web. IEEE International Conference on Tools with Artificial Intelligence. Newport Beach, California, 3–8 November1997.
Cooley, R,Mobasher, B,Srivastava, J.Grouping web page references into transactions for mining world wide web browsing patterns. IEEE Knowledge and Data Engineering Exchange Workshop. Newport Beach, California, 4 November1997,2–9.
Kosala, R,Blockeel, H.Web mining research: a survey.SIGKDD Explor2000, )2):1–15.
Pal, SK,Talwar, V,Mitra, P.Web mining in soft computing framework: relevance, state of the art and future directions.IEEE Trans Neural Netw2002, )13):1163–1177.
Zadeh, LA.Fuzzy sets.Inf Control1965, )8):338–353.
Kandel, A.Fuzzy Expert Systems.Clermont, FL: CRC Press Inc1991.
Lin, TY.Granular computing: fuzzy logic and rough sets.Comput Words Inf/Intell Syst1999, )1):183–200.
Famili, A,Shen, WM,Weber, R,Simoudis, E.Data preprocessing and intelligent data analysis.Intell Data Anal1997, )1):3–23.
Mobasher, B,Jain, N,Han, EH,Srivastava, J. Web mining: pattern discovery from world wide web transactions. Technical Report TR96–050;1996,558–567. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.57.4087%26rep=rep1%26type=pdf. (Accessed April 5, 2013).
Joshi, A,Krishnapuram, R. Robust fuzzy clustering methods to support web mining. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. Seattle, Washington, 2–4 June1998,1–8.
Robert, JS,Cooley, R,Deshp, M,Tan, PN.Web usage mining: discovery and applications of usage patterns from web data.SIGKDD Explor2000, )1):11–23.
Hong, TP,Lee, CY.Induction of fuzzy rules and membership functions from training examples.Fuzzy Sets Syst1996, )84):33–47.
Hong, TP,Chiang, MJ,Wang, SL. Mining weighted browsing patterns with linguistic minimum supports. IEEE International Conference on Systems, Man and Cybernetics. Yasmine Hammamet, Tunisia, 6–9 October2002,635–639.
Wong, C,Shiu, S,Pal, S. Mining fuzzy association rules for web access case adaptation. The International Conference on Case Based Reasoning, Workshop. Vancouver, BC, Canada, 30 July–2 August2001.
Krishnapuram, R,Joshi, A,Nasraoui, O,Yi, L.Low‐complexity fuzzy relational clustering algorithms for web mining.IEEE Trans Fuzzy Syst2001, )9):595–607.
Zhou, B,Hui, SC,Fong, ACM. Discovering and visualizing temporal‐based web access behavior. The International Conference on Web Intelligence. Compiègne University of Technology, France, 19–22 September2005.
Wu, R. Mining generalized fuzzy association rules from web logs. The International Conference on Fuzzy Systems and Knowledge Discovery. Yantai, Shandong, China, 10–12 August2010.
Hong, TP,Chiang, MJ,Wang, SL.Mining fuzzy weighted browsing patterns from time duration and with linguistic thresholds.Am J Appl Sci2008, )5):1611–1621.
Hong, TP,Huang, CM,Horng, SJ.Linguistic object‐oriented web‐usage mining.Int J Approx Reason2008, )48):47–61.
Eirinaki, M,Vazirgiannis, M.Web mining for web personalization.ACM Trans Internet Technol2003, )3):1–27.
Pierrakos, D,Paliouras, G,Papatheodorou, C,Spyropoulos, CD.Web usage mining as a tool for personalization: a survey.User Model User Adapt Interact2003, )13):311–372.
Nasraoui Olfa, FH,Joshi, A,Krishnapuram, R. Mining web access logs using relational competitive fuzzy clustering. The International Fuzzy Systems Association World Congress. Taipei, Taiwan, 17–20 August1999,195–204.
Bae, SM,Ha, SH,Park, SC.Fuzzy web ad selector based on web usage mining.IEEE Intell Syst2003, )18):62–69.
Zhou, B,Hui, SC,Fong, ACM. An effective approach for periodic web personalization. The International Conference on Web Intelligence. Hong Kong, China, 18–22 December2006.
Kim, KJ,Cho, SB.Personalized mining of web documents using link structures and fuzzy concept networks.Appl Soft Comput2007, )7):398–410.
Joshi, A,Krishnapuram, R. On mining web access logs. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. Vancouver, Canada, 9–12 June2008,63–69.
Santhisree, K,Damodaram, A. Clique: clustering based on density on web usage data: experiments and test results. The International Conference on Electronics Computer Technology. Kanyakumari, India, 8–10 April2011.
Klir, GJ,Yuan, B.Fuzzy Sets and Fuzzy Logic: Theory and Applications.Upper Saddle River, NJ: Prentice Hall Inc;1995.
Nasraoui, O,Petenes, C. Combining web usage mining and fuzzy inference for website personalization. WebKDD. Washington DC, 27 August2003.
Fong, ACM,Zhou, B,Hui, SC,Tang, J,Hong, GY.Generation of personalized ontology based on consumer emotion and behavior analysis.IEEE Trans Affec Comput2012,3:152–164.
Porcel, C,Tejeda‐Lorente, A,Martínez, MA,Herrera‐Viedma, E.A hybrid recommender system for the selective dissemination of research resources in a technology transfer office.Inf Sci2012, )184):1–19.
Chen, PM,Kuo, FC.An information retrieval system based on a user profile.J Syst Softw2000, )54):3–8.
Cheung, DW,Kao, B,Lee, J.Discovering user access patterns on the world wide web.Knowledge Based Syst1998, )10):463–470.
Abraham, A.Business intelligence from web usgae mining.J Inf Knowledge Manage2003, )2):375–390.
Wang, X,Abraham, A,Smith, KA.Intelligent web traffic mining and analysis.J Netw Comput Appl2005, )28):147–165.
Pol, K,Patil, N,Shreya, P,Chhaya, D.A survey on web content mining and extraction of structured and semistructured data.The International Conference on Emerging Trends in Engineering and Technology. Nagpur, Maharashtra, 16–18 July2008,543–546.
Agrawal, R,Srikant, R. Fast algorithms for mining association rules in large databases. The International Conference on Very Large Data Bases. Santiago de Chile, 12–15 September1994,487–499.
Hong, TP,Lee, YC.An overview of mining fuzzy association rules.Fuzzy Sets Their Extens Represent Aggreg Models2008, )220):397–410.
Lent, B,Swami, A,Widom, J. Clustering association rules. The International Conference on Data Engineering. Birmingham, 7–11 April1997.
Liu, F,Lu, Z,Lu, S.Mining association rules using clustering.Intell Data Anal2001, )5):309–326.
Agrawal, R,Srikant, R. Mining sequential patterns. The International Conference on Data Engineering. Taipei, Taiwan, 6–10 March1995.
Pei, J,Han, J,Behzad, MA,Wang, J,Helen, P,Chen, Q,Umeshwar, D,Hsu, MC.Mining sequential patterns by pattern‐growth: the prefixspan approach.IEEE Trans Knowledge Data Eng2004, )16):1424–1440.
Hong, TP,Lin, CW,Wu, YL.Incrementally fast updated frequent pattern trees.Expert Syst Appl2008, )34):2424–2435.
Hong, TP,Wu, CH.An improved weighted clustering algorithm for determination of application nodes in heterogeneous sensor networks.J Inf Hiding Multimedia Signal Process2011, )2):173–184.
Lin, CW,Hong, TP,Lu, WH.The pre‐fufp algorithm for incremental mining.Expert Syst Appl2009, )36):9498–9505.
Herrera‐Viedma, E,Pasi, G.Fuzzy approaches to access information on the web: recent developments and research trends. The Conference of the European Society for Fuzzy Logic and Technology. Zittau, Germany, 10–12 September2003,25–31.
Martín‐Bautista, MJ,Sánchez, D,Chamorro‐Martínez, J,Serrano, JM,Vila, MA.Mining web documents to find additional query terms using fuzzy association rules.Fuzzy Sets Syst2004, )148):85–104.
Chen, YL,Weng, CH.Mining fuzzy association rules from questionnaire data.Knowledge Based Syst2009, )22):46–56.
Fard, AM,Akbari, H,Mohammad, R,Akbarzadeh, T.Fuzzy adaptive resonance theory for content‐based data retrieval. Innovations in Information Technology. Dubai, 19–21 November2006.
Schockaert, S,De Cock, M,Kerre, E.Reasoning about fuzzy temporal information from the web: Towards retrieval of historical events.Soft Comput2010, )14):869–886.
Liu, W,Yan, H,Xiao, J. Automatically mining review records from forum web sites. The International Conference on Fuzzy Systems and Knowledge Discovery. Yantai, Shandong, 10–12 August2010.
Cheng, LC,Ke, ZH,Shiue, BM. Detecting changes of opinion from customer reviews. The International Conference on Fuzzy Systems and Knowledge Discovery. Shanghai, China, 26–28 July2011.
Nadali, S,Murad, MAA,Kadir, RA. Sentiment classification of customer reviews based on fuzzy logic. The International Symposium in Information Technology. Kuala Lumpur, Malaysia, 15–17 June2010.
Si, J,Wang, W. A template‐based forum posts content extraction method. The International Conference on Electrical and Control Engineering. Yichang, China, 16–18 September2011.
Subasic, P,Huettner, A.Affect analysis of text using fuzzy semantic typing.IEEE Trans Fuzzy Syst2001, )9):483–496.
Tho, QT,Hui, SC,Fong, ACM,Tru Hoang, C.Automatic fuzzy ontology generation for semantic web.IEEE Trans Knowledge Data Eng2006, )18):842–856.
Trappey, A,Trappey, CV,Fu Chiang, H,Hsiao, DW.A fuzzy ontological knowledge document clustering methodology.IEEE Trans Syst Man Cybernet B2009, )39):806–814.
Gholamzadeh, N,Taghiyareh, F. Ontology‐based fuzzy web services clustering. The International Symposium on Telecommunications. Kish Island, Iran, 4–6 December 2010, 721–725.
Kim, KJ,Cho, S‐B.Fuzzy integration of structure adaptive soms for web content mining.Fuzzy Sets Syst2004, )148):43–60.
Zadrożny, S,Kacprzyk, J,Tré, GD.Bipolar queries in textual information retrieval: a new perspective.Inf Process Manage2012, )48):390–398.
López‐Herrera, AG,Herrera‐Viedma, E,Herrera, F.Applying multi‐objective evolutionary algorithms to the automatic learning of extended boolean queries in fuzzy ordinal linguistic information retrieval systems.Fuzzy Sets Syst2009, )160):2192–2205.
Saremi, HQ,Montazer, GA.Web usability: a fuzzy approach to the navigation structure enhancement in a website system, case of Iranian civil aviation organization website.Int J Appl Math Comput Sci2005, )2):131–136.
Furnadzhiev, G.Using web sites external views for fuzzy classification.Int J Inf Theor Appl2004, )11):194–199.
Herrera‐Viedma, E,Peis, E,Morales del Castillo, JM,Alonso, S,Anaya, K.A fuzzy linguistic model to evaluate the quality of web sites that store xml documents.Int J Approx Reason2007, )46):226–253.
Leitao, L,Calado, P,Weis, M.Structure‐based inference of xml similarity for fuzzy duplicate detection. ACM Conference on Information and Knowledge Management. Lisbon, Portugal, 6–10 November2007,293–302.
Herrera‐Viedma, E,Peis, E.Evaluating the informative quality of documents in sgml format from judgements by means of fuzzy linguistic techniques based on computing with words.Inf Process Manage2003, )39):233–249.
Herrera‐Viedma, E,Pasi, G,Lopez‐Herrera, AG,Porcel, C.Evaluating the information quality of web sites: a methodology based on fuzzy computing with words: special topic section on soft approaches to information retrieval and information access on the web.J Am Soc Inf Sci Technol2006, )57):538–549.
Zhang, F,Ma, ZM,Cheng, J,Meng, X. Fuzzy semantic web ontology learning from fuzzy uml model. ACM Conference on Information and Knowledge Management. Hong Kong, China, 2–6 November2009,1007–1016.
Maio, CD,Fenza, G,Loia, V,Senatore, S.Hierarchical web resources retrieval by exploiting fuzzy formal concept analysis.Inf Process Manage2012, )48):399–418.
Kim, W,Song, YU,Hong, JS.Web enabled expert systems using hyperlink‐based inference.Expert Syst Appl2005, )28):79–91.
F¨︁urnkranz, J.Web structure mining exploiting the graph structure of the world‐wide web.ÖGAI J2002, )21):17–26.
Brin, S,Page, L.The anatomy of a large‐scale hypertextual web search engine. Computer Networks and ISDN Systems 1998, 30:107–117.
Kleinberg, JM.Authoritative sources in a hyperlinked environment.J ACM1999, )46):604–632.
Moreno, J,Morales del Castillo, J,Porcel, C,Herrera‐Viedma, E.A quality evaluation methodology for health‐related websites based on a 2‐tuple fuzzy linguistic approach.Soft Comput2010, )14):887–897.
Oreilly, T.What is web 2.0: design patterns and business models for the next generation of software.Commun Strategies2007, )1):17.
Shestakov, D,Bhowmick, SS,Lim, E‐P.Deque: querying the deep web.Data Knowledge Eng2005, )52):273–311.
Chang, KCC,Cho, J.Accessing the web: from search to integration.ACM SIGMOD. Chicago, Illinois, 27–29 June2006,804–805.
Madden, S.From databases to big data.IEEE Internet Comput2012, )16):4–6.
Atzori, L,Iera, A,Morabito, G.The internet of things: a survey.Comput Netw2010, )54):2787–2805.
Miorandi, D,Sicari, S,De Pellegrini, F,Chlamtac, I.Internet of things: vision, applications and research challenges.Ad Hoc Netw2012, )10):1497–1516.
Sarma, S,Brock, DL,Ashton, K. The networked physical world. TR MIT‐AUTOID‐WH‐001, MIT Auto‐ID Center;2000. Available at: http://www.autoidlabs.org/uploads/media/MIT‐AUTOID‐WH‐001.pdf. (Accessed April 5, 2013).
Haller, S,Karnouskos, S,Schroth, C.The internet of things in an enterprise context future internet. Future Internet2009, )5468):14–28.
Weber, RH.Internet of things‐new security and privacy challenges.Comput Law Security Rev2010, )26):23–30.

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

A survey of fuzzy web mining

Related Articles

Browse by Topic

Access to this WIREs title is by subscription only.

The latest WIREs articles in your inbox