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Understand Data Mining with easy-to-use examples
| Publisher: |
NeoNeuro |
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| Downloads: |
1 |
| Software Type: |
Freeware, 0.00 |
| File Size: |
2.09M |
| OS: |
Windows All |
| Update Date: |
13 November, 2015 |
Learn data mining with easy-to-use examples. Teach computer to add, subtract, Boolean operations, Fishers Iris task and even chess moves with convenient application NeoNeuro Data Mining!
You will be amazed how Data Mining learns chess step by step, like a child.
Unlike neural nets NeoNeuro Data Mining works fast, can answer 'I do not know' to some questions and manages with multidimensional tasks.
Features:
Clearness,
High speed,
Working with missed values,
Common algorithm solves different types of tasks.
The program can give a single-value answer, several variants or reply 'I do not know'.
NeoNeuro Data Mining takes into consideration the interconnection between different parameters: in chess it is possible to connect coordinates (vertical for move FROM and vertical for move TO), in financial analysis it is possible to connect data about money to distinguish them from non-money parameters. For instance, salary and monthly credit fee both are the same dimension money, but place of employment and age are the dimensions (attributes) of other types. The problem of neural nets is that they perceive salary, monthly fee and place of employment as three different notions. This is a great restriction which is fixed in Data Mining.
Learning of NeoNeuro Data Mining is similar to childs learning. The application makes the same human mistakes which can be seen in chess learning.
NeoNeuro Data Mining is recommended not only for the purpose of students teaching but also for solving difficult data mining tasks in science research.
NeoNeuro Data Mining is convenient for helping students in understanding the following courses: artificial intelligence, machine learning, neural nets and numerical methods of data mining.
Due to its strong logic and geometry learning skills, NeoNeuro Data Mining is designed for solving tasks in robotics. It is also recommended for analysis of non-structured data in medicine, finance, biology, etc.
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