Fuad Aleskerov  – Head of Department of Mathematics, Faculty of Economics, National Research University Higher School of Economics; head of laboratory, Trapeznikov Institute of Control Sciences, Russian Academy of Sciences.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: alesk@hse.ru

Veronika Belousova  – Head of Budgeting Methodology Department, Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: vbelousova@hse.ru

Ludmila Egorova  – Lecturer, Department of Mathematics, Faculty of Economics, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: legorova@hse.ru

Boris Mirkin  – Professor, Department of Data Analysis and Artificial Intelligence, School of Applied Mathematics and Information Science, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: bmirkin@hse.ru

In this paper the term ‘pattern’ is referred to a combination of values of some features such that these feature values define a group of objects that ​​significantly differ from other objects. In the paper’s first part, we presented a review of the literature on each of the three main aspects of this concept: (1) examples of usage of the concept of ‘pattern’ in sciences and technology, (2) methods of cluster analysis, with an eye over those relevant, and (3) dynamics of multi-dimensional objects. Then we proposed three different, but mathematically equivalent, definitions of the concept of pattern using (a) parallel coordinates, (b) conjunctive descriptions, and (c) geometric ‘boxes’.
In this part we give a representative sample of examples of static and dynamic analysis of patterns, within the most common framework of parallel coordinates. The static pattern analysis is a two-step method of the automated pattern formation. At the first stage we use classical cluster analysis to find clusters of objects, and at the second stage we find patterns that adequately represent the obtained clusters. Dynamic analysis of patterns is to highlight the types of functional stability of objects depending on how frequently an object changes between the patterns to which it belongs at different time periods. This typology may help in managing objects; also, it allows us to determine groups of risk consisting of objects changing too frequently.
Specifically, we build and demonstrate patterns in data for: (a) comparative macroeconomic analysis, (b) evaluation of efficiency and analysis of business models attended to by banks in Turkey and Russia, (c) patterns of voting behavior and electoral change in General Elections in the UK and Municipal Elections in Finland, and (d) issues of innovation in the development of regions in the Russian Federation in the long run. This review outlines a number of research projects at which authors participated. Some of these projects had been conducted before the concept of pattern was developed in full. In this sense the current paper generalizes those earlier publications.