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Efficient mining of discriminative molecular fragments

Di Fatta, G. and Berthold, M. R. (2005) Efficient mining of discriminative molecular fragments. In: 17th IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS-05), 14-16 Nov 2005, Phoenix, AZ, USA, pp. 619-625.

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Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

Item Type:Conference or Workshop Item (Paper)
Divisions:No Reading authors. Back catalogue items
Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:6154
Uncontrolled Keywords:biochemical databases, distributed computing, dynamic load balancing, frequent subgraph mining


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