ACM Computing Surveys (CSUR)
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Using Markov models for web site link prediction
Proceedings of the thirteenth ACM conference on Hypertext and hypermedia
Predicting category accesses for a user in a structured information space
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Using Markov Chains for Link Prediction in Adaptive Web Sites
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
THESUS: Organizing Web document collections based on link semantics
The VLDB Journal — The International Journal on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Clustering documents in a web directory
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Evaluating the markov assumption for web usage mining
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A unified framework for model-based clustering
The Journal of Machine Learning Research
Building Association-Rule Based Sequential Classifiers for Web-Document Prediction
Data Mining and Knowledge Discovery
Characterizing customer groups for an e-commerce website
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Web personalization integrating content semantics and navigational patterns
Proceedings of the 6th annual ACM international workshop on Web information and data management
Web personalization based on static information and dynamic user behavior
Proceedings of the 6th annual ACM international workshop on Web information and data management
A clickstream-based collaborative filtering personalization model: towards a better performance
Proceedings of the 6th annual ACM international workshop on Web information and data management
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Combining queueing networks and web usage mining techniques for web performance analysis
Proceedings of the 2005 ACM symposium on Applied computing
Web path recommendations based on page ranking and Markov models
Proceedings of the 7th annual ACM international workshop on Web information and data management
IEEE Transactions on Knowledge and Data Engineering
WAM-Miner: in the search of web access motifs from historical web log data
Proceedings of the 14th ACM international conference on Information and knowledge management
Supervised clustering with support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Object prefetching using semantic links
ACM SIGMIS Database
A method for personalized clustering in data intensive web applications
Proceedings of the joint international workshop on Adaptivity, personalization & the semantic web
K-means clustering versus validation measures: a data distribution perspective
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An overhead and resource contention aware analytical model for overloaded web servers
WOSP '07 Proceedings of the 6th international workshop on Software and performance
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
A framework of combining Markov model with association rules for predicting web page accesses
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Mining association rules using clustering
Intelligent Data Analysis
Integrating recommendation models for improved web page prediction accuracy
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
Combining markov models and association analysis for disease prediction
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
A theoretical model for obfuscating web navigation trails
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Novel Approaches for Integrating MART1 Clustering Based Pre-Fetching Technique with Web Caching
International Journal of Information Technology and Web Engineering
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Accurate next web page prediction benefits many applications, e-business in particular. The most widely used techniques for this purpose are Markov Model, association rules and clustering. However, each of these techniques has its own limitations, especially when it comes to accuracy and space complexity. This paper presents an improved prediction accuracy and state space complexity by using novel approaches that combine clustering, association rules and Markov Models. The three techniques are integrated together to maximise their strengths. The integration model has been shown to achieve better prediction accuracy than individual and other integrated models.