Algorithms for clustering data
Algorithms for clustering data
How to build a Beowulf: a guide to the implementation and application of PC clusters
How to build a Beowulf: a guide to the implementation and application of PC clusters
External memory algorithms and data structures
External memory algorithms
High Performance Cluster Computing: Programming and Applications
High Performance Cluster Computing: Programming and Applications
Clustering Algorithms
Mining Very Large Databases with Parallel Processing
Mining Very Large Databases with Parallel Processing
Linux Kernel Internals with Cdrom
Linux Kernel Internals with Cdrom
Computer
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Parallel k/h-Means Clustering for Large Data Sets
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
A Data-Clustering Algorithm on Distributed Memory Multiprocessors
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Enhancing the Apriori Algorithm for Frequent Set Counting
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Exploiting idle cycles to execute data mining applications on clusters of PCs
Journal of Systems and Software
An efficient parallel and distributed algorithm for counting frequent sets
VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science
Compiler and middleware support for scalable data mining
LCPC'01 Proceedings of the 14th international conference on Languages and compilers for parallel computing
| Hi-index | 0.00 |
This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of w orkstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.