A Review on Clustering Techniques

The World Wide Web keeps on developing vault of site pages and connections at an exponential rate which makes abusing all valuable data a standing test. It has as of late an extensive variety of uses in E-trade site and E-administrations, for example, building intelligent showcasing systems, Web proposal and Web personalization. Web utilization mining is the way toward separating helpful use designs from the web information. Web personalization utilizes web utilization digging strategy for the procedure of information obtaining done by investigating the client navigational examples premium. These days, the Web is an essential wellspring of data recovery, and the clients getting to the Web are from various foundations. The use data about clients is recorded in web logs. Examining web log documents to extricate helpful examples is called Web Usage Mining. Web use mining approaches incorporate bunching, affiliation lead mining, successive example mining and so forth. This article gives a study of the accessible writing on Web utilization mining and audits the exploration and application issues in web use mining. Keywords— Web usage mining, server log file, web logs, clustering, fuzzy logic.

[1]  Pier Luca Lanzi,et al.  Mining interesting knowledge from weblogs: a survey , 2005, Data Knowl. Eng..

[2]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[3]  Raymond Y. K. Lau,et al.  An ontology-based Web mining method for unemployment rate prediction , 2014, Decis. Support Syst..

[4]  John Edwards,et al.  Personalised online sales using web usage data mining , 2007, Comput. Ind..

[5]  Florent Masseglia,et al.  Web Usage Mining: Sequential Pattern Extraction with a Very Low Support , 2004, APWeb.

[6]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[7]  Mario A. Góngora,et al.  Web usage mining with evolutionary extraction of temporal fuzzy association rules , 2013, Knowl. Based Syst..

[8]  N. M. Varghese,et al.  Cluster optimization for enhanced web usage mining using fuzzy logic , 2012, 2012 World Congress on Information and Communication Technologies.

[9]  Anthony J. T. Lee,et al.  Mining Web navigation patterns with a path traversal graph , 2011, Expert Syst. Appl..

[10]  Ming Li,et al.  An approach of product usability evaluation based on Web mining in feature fatigue analysis , 2014, Comput. Ind. Eng..

[11]  Philip S. Yu,et al.  On the Use of Side Information for Mining Text Data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[12]  Prakash S. Raghavendra,et al.  Comparative study of neural networks and k-means classification in web usage mining , 2010, 2010 International Conference for Internet Technology and Secured Transactions.

[13]  Sungjune Park,et al.  Sequence-based clustering for Web usage mining: A new experimental framework and ANN-enhanced K-means algorithm , 2008, Data Knowl. Eng..

[14]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[15]  Huiping Peng,et al.  Discovery of Interesting Association Rules Based on Web Usage Mining , 2010, 2010 International Conference on Multimedia Communications.

[16]  Panagiotis Germanakos,et al.  Modeling users on the World Wide Web based on cognitive factors, navigation behavior and clustering techniques , 2013, J. Syst. Softw..

[17]  Giovanna Castellano,et al.  NEWER: A system for NEuro-fuzzy WEb Recommendation , 2011, Appl. Soft Comput..

[18]  Xiaozhe Wang,et al.  Intelligent web traffic mining and analysis , 2005, J. Netw. Comput. Appl..