Аннотация:
The article presents results of the intelligent data clustering system for searching hidden
regularities in financial transactions development. The main aspects and problems of
increasing the volume of financial information within the client base segmentation for the
formation of various development strategies and marketing methods development for
promoting goods in order to expand the target audience are given. The key opportunities and
difficulties of using modern data mining methods and algorithms based on supervised and
unsupervised learning are described and analyzed. Existing hybridization approaches
implementation for data analysis algorithms, including those based on the use of data
clustering ensembles, are considered. The concept of data analysis stages in the process of
solving the segmentation problem is proposed, research metrics are formalized, clustering
algorithms are selected and programmatically implemented via information system with the
assignment clusters initial number and calculating it independently. Collected and formed
balanced set of data on financial transactions for research, performed its statistical analysis,
transformation and preparation for clustering. A software implementation of the system has
been developed and its key functionality, component composition has been designated. The
developed algorithms results studies based on the summary matrix of feature proximity
analysis are presented, a unified space for cluster visualization is created based on the t-SNE
approach, clustering quality assessing metrics are calculated.
