DATA CLUSTERING BASED ON INDUCTIVE LEARNING OF NEURO-FUZZY NETWORK WITH DISTANCE HASHING

Context. Cluster analysis is widely used to analyze data of various nature and dimensions. However, the known methods of cluster analysis are characterized by low speed and are demanding on computer memory resources due to the need to calculate pairwise distances between instances in a multidimensional feature space. In addition, the results of known methods of cluster analysis are difficult for human perception and analysis with a large number of features. 
Objective. The purpose of the work is to increase the speed of cluster analysis, the interpretability of the resulting partition into clusters, as well as to reduce the requirements of cluster analysis to computer memory. 
Method. A method for cluster analysis of multidimensional data is proposed, which for each instance calculates its hash based on the distance to the conditional center of coordinates, uses a one-dimensional coordinate along the hash axis to determine the distances between instances, considers the resulting hash as a pseudo-output feature, breaking it into intervals, which matches the labels pseudo-classes – clusters, having received a rough crisp partition of the feature space and sample instances, automatically generates a partition of input features into fuzzy terms, determines the rules for referring instances to clusters and, as a result, forms a fuzzy inference system of the Mamdani-Zadeh classifier type, which is further trained in the form of a neuro-fuzzy network to ensure acceptable values of the clustering quality functional. This makes it possible to reduce the number of terms and features used, to evaluate their contribution to making decisions about assigning instances to clusters, to increase the speed of data cluster analysis, and to increase the interpretability of the resulting data splitting into clusters. 
Results. The mathematical support for solving the problem of cluster data analysis in conditions of large data dimensions has been developed. The experiments confirmed the operability of the developed mathematical support have been carried out. 
Conclusions. . The developed method and its software implementation can be recommended for use in practice in the problems of analyzing data of various nature and dimensions.