Journal
JOURNAL OF BIOLOGICAL SYSTEMS
Volume 18, Issue 4, Pages 787-807Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218339010003512
Keywords
Association Rules Mining; Concept Lattices; Formal Concept Analysis; Singular Value Decomposition
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Funding
- Dept. of Science and Technology, Govt. of India [SR/S3/EECE/25/2005]
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In recent times Formal Concept Analysis (FCA), in which the data is represented as a formal context, has gained popularity for Association Rules Mining (ARM). Application of ARM in health care datasets is challenging and a highly rewarding problem. However, datasets in the medical domain are of high dimension. As the dimensionality of dataset increases, size of the formal context as well as complexity of FCA based ARM also increases. To handle the problem of high dimensionality and mine the associations, we propose to apply Singular Value Decomposition (SVD) on the dataset to reduce the dimensionality and apply FCA on the reduced dataset for ARM. To demonstrate the proposed method, experiments are conducted on Tuberculosis (TB) and Hypertension (HP) datasets. Results indicate that with fewer concepts, SVD based FCA has achieved the performance of FCA on TB data and performed better than FCA on HP data.
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