Intelligent data clustering system for searching hidden regularities in financial transactions

Abstract

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.