Abstract
There are various methods of objects’ clusterization used in different areas of machine learning. Among the vast amount of clusterization methods, the K-means method is one of the most popular. Such a method has as pros as cons. Speaking about the advantages of this method, we can mention the rather high speed of objects clusterization. The main disadvantage is a necessity to know the number of clusters before the experiment. This paper describes the new way and the new method of clusterization, based on the K-means method. The method we suggest is also quite fast in terms of processing speed, however, it does not require the user to know in advance the exact number of clusters to be processed. The user only has to define the range within which the number of clusters is located. Besides, using suggested method there is a possibility to limit the radius of clusters, which would allow finding objects that express the criteria of one cluster in the most distinctive and accurate way, and it would also allow limiting the number of objects in each cluster within the certain range.
Highlights
Nowadays artificial intelligence is a very popular tool in various fields of science - economics, public life and production
Bayesian networks are widely used in such areas as economics, psychology, sociology, medicine, genetics, management theory, etc
We have described two types of the Bayesian networks’ learning below: Probabilistic aspects of machine learning and the basis of various algorithms used in machine learning are described in [9]
Summary
Nowadays artificial intelligence is a very popular tool in various fields of science - economics, public life and production. Sometimes researcher may have some doubts about the necessity of determining Bayesian networks’ separate nodes individual nodes of a Bayesian network These problems are solved by involving specialists in the studied area. The processing of big amount of data in the construction of Bayesian networks significantly complicates an already difficult task This implies new problems in the computational area but in mathematics. Under Bayesian networks’ controlled learning we usually understood as different ways to determine the probabilistic characteristics of the separate nodes’ variables of the network, as well as the probabilistic dependencies between separate nodes based on some array of experimental data. Under Bayesian networks’ uncontrolled learning we usually understood methods of defining new nodes of the network and new dependencies between nodes based on some array of experimental data. We assume to apply this software to grant project «Development and software implementation of a package for solving applied problems in Bayesian networks»
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
