Abstract: A golden effect pigment comprising an optionally passivated platelet-shaped metallic substrate and an iron oxide layer, wherein the effect pigment has a hue angle h15 of 67°?h15?78° and a chroma C*15 of ?90 is provided. Further, a golden effect pigment comprising an optionally passivated platelet-shaped metallic substrate and an iron oxide layer, wherein the effect pigment has a hue angle h15 of 67°?h15?78° and a chroma C*45 of ?50 is provided. The golden effect pigments are highly chromatic and suitable for coloring a coating composition such as a paint, a printing ink, an ink, a varnish, plastics, a fiber, a film or a cosmetic preparation, preferably an automotive, an architectural or an industrial coating composition.

Abstract: A golden effect pigment comprising an optionally passivated platelet-shaped metallic substrate and an iron oxide layer, wherein the effect pigment has a hue angle h15 of 67°?h15?78° and a chroma C*15 of ?90 is provided. Further, a golden effect pigment comprising an optionally passivated platelet-shaped metallic substrate and an iron oxide layer, wherein the effect pigment has a hue angle h15 of 67°?h15?78° and a chroma C*45 of ?50 is provided.

Abstract: One or more data mining processes may be executed based on control parameters to discover a plurality of result patterns in a data set. The discovered result patterns are presented to a user. Information on one or more selected result patterns, where the selection involves the user's subjective interest, is received. The control parameters are automatically updated based on the received information on the selected result patterns.

Abstract: One or more data mining processes may be executed based on control parameters to discover a plurality of result patterns in a data set. The discovered result patterns are presented to a user. Information on one or more selected result patterns, where the selection involves the user's subjective interest, is received. The control parameters are automatically updated based on the received information on the selected result patterns.

Abstract: Shaped catalyst bodies comprising a multielement oxide I having the general stoichiometry I [Bi1WbOx]a[Mo12Z1cZ2dFeeZ3fZ4gZ5hOy]1 (I), where Z1=Ni or Co, Z2=alkali metal or alkaline earth metal, Z3=zinc, phosphorus, arsenic, boron, antimony, tin, cerium, vanadium, chromium or bismuth, Z4=silicon, aluminum, titanium, tungsten or zirconiurn, Z5=copper, silver, gold, yttrium, lanthanum and lanthanides, a=0.1 to 3, b=0.1 to 10, c=1 to 10, d=0.01 to 2, e=0.01 to 5, f=0 to 5, g=0 to 10, h=0 to 1 and x, y=numbers determined by the valence and abundance of the elements other than oxygen in I, as active composition are produced by preforming a mixed oxide Bi1WbOx by coprecipitation from an aqueous environment at a pH in the range from 1.

Abstract: One or more data mining processes may be executed based on control parameters to discover a plurality of result patterns in a data set. The discovered result patterns are presented to a user. Information on one or more selected result patterns, where the selection involves the user's subjective interest, is received. The control parameters are automatically updated based on the received information on the selected result patterns.

Abstract: Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as n-best hypotheses problems where the goal is to find the n hypotheses from a given hypothesis space that score best according to a certain utility function. We present a sampling algorithm that solves this problem by issuing a small number of database queries while guaranteeing precise bounds on confidence and quality of solutions. Known sampling approaches have treated single hypothesis selection problems, assuming that the utility be the average (over the examples) of some function—which is not the case for many frequently used utility functions. We show that our algorithm works for all utilities that can be estimated with bounded error. We provide these error bounds and resulting worst-case sample bounds for some of the most frequently used utilities, and prove that there is no sampling algorithm for a popular class of utility functions that cannot be estimated with bounded error.

Abstract: Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as n-best hypotheses problems where the goal is to find the n hypotheses from a given hypothesis space that score best according to a certain utility function. We present a sampling algorithm that solves this problem by issuing a small number of database queries while guaranteeing precise bounds on confidence and quality of solutions. Known sampling approaches have treated single hypothesis selection problems, assuming that the utility be the average (over the examples) of some function—which is not the case for many frequently used utility functions. We show that our algorithm works for all utilities that can be estimated with bounded error. We provide these error bounds and resulting worst-case sample bounds for some of the most frequently used utilities, and prove that there is no sampling algorithm for a popular class of utility functions that cannot be estimated with bounded error.

Abstract: The method is for detecting a selectable number of groups of objects having at least one selectable characteristic from a population of objects specifiable by a plurality of attributes. The objects of the population are subdivided into object groups of a first order, on the basis of respectively at least one attribute. The quality of each object group of this order is detected, on the basis of the total number of its objects and the number of its objects having said at least one characteristic. Each object group of this order is included into the number of object groups to be detected, if the object group has a quality higher than the lowest quality of the object group among the object groups detected up to this point. A hypothetical quality is detected on the basis exclusively of those objects of this object group which have at least one of the characteristics.