Page 34 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062934Distance on Numeric Data: Minkowski Distance: we describe distance measures that are commonly used for computing the dissimilarity of objects described by numeric attributes. These measures include the Euclidean, Manhattan, and Minkowski distances. For more examples visit: https://www.youtube.com/watch?v=ofIfXMPem2M3- Proximity Measures for Ordinal Attributes Ordinal attributes may also be obtained from the discretization of numeric attributes by splitting the value range into a finite number of categories. These categories are organized into ranks. That is, the range of a numeric attribute can be mapped to an ordinal attribute f having Mf states. For example, the range of the interval-scaled attribute temperature (in Celsius) can be organized into the following states: %u221230 to %u221210, %u221210 to 10, 10 to 30, representing the categories cold temperature, moderate temperature, and warm temperature, respectively. Let M represent the number of possible states that an ordinal attribute can have. These ordered states define the ranking 1,..., Mf, The treatment of ordinal attributes is quite similar to that of numeric attributes when computing dissimilarity between objects. Suppose that f is an attribute from a set of ordinal attributes describing n objects. The dissimilarity computation with respect to f involves the following steps: 1.The value of f for the ith object is xif , and f has Mf ordered states, representing the ranking 1,..., Mf. Replace each xif by its corresponding rank, rif {1,..., Mf }. 2. Since each ordinal attribute can have a different number of states, it is often necessary to map the range of each attribute onto [0.0, 1.0] so that each attribute has equal weight. We perform such data normalization by replacing the rank rif of the ith object in the f th attribute by rif %u22121zif = M f %u22121 3.Dissimilarity can then be computed using any of the distance measures for numeric attributes, using zif to represent the f value for the ith object.