Algorithms for clustering data
Algorithms for clustering data
Run-Time Parallelization and Scheduling of Loops
IEEE Transactions on Computers
Compiler and software distributed shared memory support for irregular applications
PPOPP '97 Proceedings of the sixth ACM SIGPLAN symposium on Principles and practice of parallel programming
Eliminating synchronization bottlenecks in object-based programs using adaptive replication
ICS '99 Proceedings of the 13th international conference on Supercomputing
Proceedings of the 14th international conference on Supercomputing
Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel data mining for association rules on shared-memory multi-processors
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Parallel data mining for association rules on shared memory systems
Knowledge and Information Systems
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Parallel Programming with Polaris
Computer
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
On the Automatic Parallelization of Sparse and Irregular Fortran Programs
LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
Parallel Classification for Data Mining on Shared-Memory Multiprocessors
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
IEEE Transactions on Knowledge and Data Engineering
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Data mining techniques focus on finding novel and useful patterns or models from large datasets. Because of the volume of the data to be analyzed, the amount of computation involved, and the need for rapid or even interactive analysis, data mining applications require the use of parallel machines. We have been developing compiler and runtime support for developing scalable implementations of data mining algorithms. Our work encompasses shared memory parallelization, distributed memory parallelization, and optimizations for processing disk-resident datasets. In this paper, we focus on compiler and runtime support for shared memory parallelization of data mining algorithms. We have developed a set of parallelization techniques that apply across algorithms for a variety of mining tasks. We describe the interface of the middleware where these techniques are implemented. Then, we present compiler techniques for translating data parallel code to the middleware specification. Finally, we present a brief evaluation of our compiler using apriori association mining and k-means clustering.