华工计算机大三上学习人工智能外教part.pptx

Design of information granulesOutlineThe principle of justifiable granularityConstruction of information granules via fuzzy clusteringKnowledge-based clusteringSuccessive refinements of information granulesThe principle of justifiable granularityGivencollection of a one-dimensional (scalar) numeric data, D = {x1, x2, …, xN}. form a meaningful (legitimate) information granule based which adheres to two intuitively compelling requirements: experimental evidence (legitimacy) (ii) sound semantic (meaning) The principle of justifiable granularity – experimental evidenceexperimental evidence (legitimacy): The numeric evidence accumulated within the bounds of W has to be as high as possible. The principle of justifiable granularity – semantic soundness(ii) sound semantic (meaning)information granule should be as specific as possible. This request implies that the resulting information granule comes with a well-defined semantics (meaning). We would like to have W highly detailed, which makes the information granule semantically meaningful (sound). This implies that the smaller (more compact) the information granule (lower information granule) is, the better. This point of view is in agreement with our general perception of knowledge being articulated through constraints (information granules) “x is in [1,3]” is more specific (semantically sound, more supportive of any further action, etc.) than “x is in [0, 10]”. The principle of justifiable granularity – conflicting requirementsPrinciple of justifiable granularityGroup of recordings of age of individuals in a certain communityinformation granule == piece of knowledge, constraints (restriction) over space of ageInformation granule [1, 110] high experimental evidence, missing semantics Information granule {47.55} no experimental evidence, visible semantics The principle of justifiable granularity –optimization aspects Construction of interval information granuleOptimization of upper bound (optimization do