1. A computer-operable method for data processing involved in but not limited to image, pattern and sequence recognition, decision-making and machine-learning, comprising the steps of: (a) generation of a hypothesis-parameter, in addition to parameters already existing in a database, both for a reference object (or reference objects) to be used as benchmark(s) in data processing, and for all other objects in a database that are subjects of a comparative analysis (hereunder referred to as target objects); (b) assigning of digital values to reference objects in a hypothesis-parameter, such digital values being a reflection of a certain hypothesis of a relationship between said reference objects—based on either an a priori existing idea or a result of a preliminary experimental study, including clustering, of objects covered by said hypothesis-parameter; (c) assigning of certain digital values to all target objects in a hypothesis-parameter; (d) using a hypothesis-parameter in clustering of objects, along with plurality of other parameters describing objects under clustering, (e) establishing a number of copies (hereunder referred to as multiplication number) of hypothesis-parameter required for compensation, during a clustering process, of effect of all other parameters describing a given object so that clustering based on thus established number of copies of a hypothesis-parameter along with the rest of parameters is identical to clustering produced upon use of a hypothesis-parameter as the only parameter, (f) consecutive addition of each target object to a reference object (or reference objects); and (g) using an established multiplication number for measurement of dissimilarity between reference object(s) and target objects, hence verification of validity of a hypothesis underlying a generated hypothesis-parameter.