A parameterised algorithm for mining association rules
ADC '01 Proceedings of the 12th Australasian database conference
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Strategies for Parallel Data Mining
IEEE Concurrency
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Grids as Production Computing Environments: The Engineering Aspects of NASA's Information Power Grid
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Mining Sequential Patterns Using Graph Search Techniques
COMPSAC '03 Proceedings of the 27th Annual International Conference on Computer Software and Applications
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Parallel tree-projection-based sequence mining algorithms
Parallel Computing
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
Parallel mining of closed sequential patterns
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
LAPIN-SPAM: An Improved Algorithm for Mining Sequential Pattern
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Grid implementation of the Apriori algorithm
Advances in Engineering Software
A grid-based approach for enterprise-scale data mining
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Design and implementation of a data mining grid-aware architecture
Future Generation Computer Systems - Special section: Data mining in grid computing environments
A grid-based approach for enterprise-scale data mining
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Distributed data mining in grid computing environments
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Grid-enabling data mining applications with DataMiningGrid: An architectural perspective
Future Generation Computer Systems
Service-oriented middleware for distributed data mining on the grid
Journal of Parallel and Distributed Computing
Middleware for data mining applications on clusters and grids
Journal of Parallel and Distributed Computing
An improved data mining approach using predictive itemsets
Expert Systems with Applications: An International Journal
A load-balanced distributed parallel mining algorithm
Expert Systems with Applications: An International Journal
Parallel TID-based frequent pattern mining algorithm on a PC Cluster and grid computing system
Expert Systems with Applications: An International Journal
Perspectives on grid computing
Future Generation Computer Systems
Distributed data mining on grids: services, tools, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining generalized temporal patterns based on fuzzy counting
Expert Systems with Applications: An International Journal
Efficient algorithms for frequent pattern mining in many-task computing environments
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Mining sequential patterns (MSP) is an important task for knowledge discovery and data mining (KDD). Like in most KDD tasks, MSP also invokes a number of iterations for generating, adjusting, and comparing data. This paper presents an empirical study on deploying MSP in a grid computing environment and demonstrates the effectiveness and performance improvements gained in this deployment. GSP, which is a typical MSP method, is used as the mining algorithm to be investigated. A grid computing environment is designed and implemented, where all GSP functions are organized as loosely coupled web-services. MSP is achieved through the cooperation of these web-services using the divide-and-conquer strategy. Several monitoring mechanisms are developed to help manage the MSP process. The experimental results show that the proposed grid computing environment provides a flexible and efficient platform for MSP.