Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Automatic image placement to provide a guaranteed frame rate
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Opening the black box: interactive hierarchical clustering for multivariate spatial patterns
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
IEEE Transactions on Knowledge and Data Engineering
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Clustering Algorithm for Market Basket Data Based on Small Large Ratios
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Evaluation of web usage mining approaches for user's next request prediction
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
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Many algorithms are suggested for improving the performance of a walkthrough system which contains large-scale VRML models.Since massive objects are stored in the storage systems, and may be scattered, this situation increases the search time to access the objects.However, traditional walkthrough system never considers the problem of how to reduce access times of objects in the storage systems.The quality of the walkthrough system needs to be improved in order to meet the user's demand. In this paper, we present an efficient mining method to improve the efficiency of object accesses.Meanwhile, clustering methodology is particularly appropriate for the exploration of interrelationships among objects to reduce the access time.In other words, prediction and accuracy are our major concerns for improving the system performance.Also, we introduce the relationship measures among transactions, views and objects.Based on these relationship measures, the clustering algorithm will determine how to cluster and the optimal physical organization of those VRML objects on disks.Besides, we suggest two clustering criteria - intra-pattern similarity matrix and inter-pattern frequency table.Our experimental evaluation on the walkthrough data set shows that our algorithm doesn't only significantly cut down the access time, but also enhance the accuracy of data prefetch.