NeoNeuro Data Mining is a user-friendly software that enables users to learn data mining through simple examples. This application offers easy-to-use features such as adding, subtracting, Boolean operations, and Fishers Iris task, as well as the ability to teach computer chess moves. Users can witness how the software learns chess progressively.
NeoNeuro Data Mining is an easy-to-use application that allows users to learn data mining with effective examples. With this convenient software, you are able to teach your computer to add, subtract, perform Boolean operations, solve Fisher's Iris task, and even make chess moves! One of the most fascinating features of this program is its ability to learn chess step by step, similar to how a child would learn.
What's more, NeoNeuro Data Mining works at a high speed and is capable of answering "I do not know" when faced with certain questions. Additionally, this program has the ability to manage with multidimensional tasks and work with missed values. It also features a common algorithm that solves different types of tasks, giving users a single-value answer or several variants.
One of the key benefits of NeoNeuro Data Mining is its ability to take into consideration the interconnection between different parameters. For instance, in chess, you can connect coordinates (vertical for move FROM and vertical for move TO), and in financial analysis, you can connect data about money to differentiate them from non-money parameters. This software can distinguish between dimensions, such as salary and monthly credit fee, which are both of the same dimension money.
Moreover, NeoNeuro Data Mining is similar to the learning process of a child. The program makes similar human mistakes that can be seen in chess learning. As such, it is not only useful for teaching students but also for solving difficult data mining tasks in science research. It is also ideal for helping students understand courses in artificial intelligence, machine learning, neural nets, and numerical methods of data mining.
Designed for solving tasks in robotics, NeoNeuro Data Mining has strong logic and geometry learning skills. In addition, it is recommended for the analysis of non-structured data in medicine, finance, biology, and other fields. Its many useful features and applications make NeoNeuro Data Mining a valuable tool for anyone seeking to learn data mining with ease.
What's more, NeoNeuro Data Mining works at a high speed and is capable of answering "I do not know" when faced with certain questions. Additionally, this program has the ability to manage with multidimensional tasks and work with missed values. It also features a common algorithm that solves different types of tasks, giving users a single-value answer or several variants.
One of the key benefits of NeoNeuro Data Mining is its ability to take into consideration the interconnection between different parameters. For instance, in chess, you can connect coordinates (vertical for move FROM and vertical for move TO), and in financial analysis, you can connect data about money to differentiate them from non-money parameters. This software can distinguish between dimensions, such as salary and monthly credit fee, which are both of the same dimension money.
Moreover, NeoNeuro Data Mining is similar to the learning process of a child. The program makes similar human mistakes that can be seen in chess learning. As such, it is not only useful for teaching students but also for solving difficult data mining tasks in science research. It is also ideal for helping students understand courses in artificial intelligence, machine learning, neural nets, and numerical methods of data mining.
Designed for solving tasks in robotics, NeoNeuro Data Mining has strong logic and geometry learning skills. In addition, it is recommended for the analysis of non-structured data in medicine, finance, biology, and other fields. Its many useful features and applications make NeoNeuro Data Mining a valuable tool for anyone seeking to learn data mining with ease.
What's New
Version 2.1:
Confusion Matrix for Statistics
Examples: Adults, Abalone, Credit, etc.