By Piotr Hońko
This publication presents basic granular computing techniques to mining relational facts, the 1st of which makes use of summary descriptions of relational items to construct their granular illustration, whereas the second one extends present granular information mining options to a relational case.
Both techniques give the opportunity to accomplish and increase well known information mining projects resembling category, clustering, and organization discovery. How can diversified relational information mining initiatives top be unified? How can the development technique of relational styles be simplified? How can richer wisdom from relational facts be came across? these types of questions might be responded within the comparable approach: through mining relational facts within the paradigm of granular computing!
This publication will permit readers with prior adventure within the box of relational information mining to find the numerous advantages of its granular standpoint. In flip, these readers accustomed to the paradigm of granular computing will locate precious insights on its program to mining relational information. finally, the ebook deals all readers attracted to computational intelligence within the broader experience the chance to deepen their realizing of the newly rising box granular-relational information mining.
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Sample text
Consider also different generalizations rltgen 1 {1, 3}, _))} and rltgen (o3 ) = {purchase(B, A, _, 4, _))}. Let B0 = {prod_id}. 20 we obtain simB0 (purchase(B, A, C, {1, 3}, _), purchase(B, A, C, 4, _)) = sim({1, 3}, {4}) = 0. 25. Customer 2 bought two products, whereas customer 3—one. 5. 25. 3 The measure can be used for sets of positive numbers only. 48 5 Rough-Granular Computing Let attributes age and income be generalized as follows age1 = {25-30}, age2 = {30-35}, age3 = {36-40}, inc1 = {1500-2000}, inc2 = {2500-3000}.
Syntactic comparison of abstract objects descriptions is possible if they are constructed in the same way regardless of the depth level. Therefore, the following assumption is made. 1 ∀ ∀ rlt i (o)σi = rlt i (o)σj 1≤i G. a generalization of customer(A, _, _, _, _, _) ∧ purchase(B, A, C, {1, 2}, _) is customer (A, _, _, _, _, _). Furthermore, the condition from steps 5 and 6 that a pattern has to be frequent may be omitted in the bottom-up case. The condition is satisfied according to the following property: Any generalization of a frequent pattern is frequent. The approach is illustrated by the following example. 2. 3. We use the following constraints during the construction of patterns: 1. id), _, _, _, _, +type(class))), 2.