Some algorithms for data table (re)ordering using monotone systems

We present here some algorithms for data table reordering to give it more informative form. All these algorithms are Monotone System algorithms and have been created in our department within several years. Main aim of these algorithms is to summarize entire data table with one "average" object called best decision (BD) and to open the hidden inner structure of the data table. The BD object is defined here as one that gives the maximal value to the weight function. At first we give a review of a computationally simple weight function to define BD which does not account for the dependencies between the attributes and we describe a method named Scale of Conformity to implement it. We describe several table reordering techniques based on that scale. Finally we define a BD as a branch in the decision tree and introduce a weight function that takes attribute dependencies into account. We present some examples to demonstrate effectiveness of such techniques.