Abstract
A contingency table summarizes the conditional frequencies of two attributes and shows how these two attributes are dependent on each other. Thus, this table is a fundamental tool for pattern discovery with conditional probabilities, such as rule discovery. In this paper, a contingency table is interpreted from the viewpoint of granular computing. The first important observation is that contingency tables compare two attributes with respect to granularity, which means that a n×n table compares two attributes with the same granularity, while a m×n(m ≥ n) table can be viewed as the projection from m-partitions to n partition. The second important observation is that matrix algebra is a key point of analysis of this table. Especially, the degree of independence, rank plays a very important role in extracting a probabilistic model from a given contingency table.
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Tsumoto, S. (2003). Linear Independence in Contingency Table. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_47
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DOI: https://doi.org/10.1007/3-540-39205-X_47
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