Data Mining and Rule-Based Techniques in Random Decision Forest

Running head: DATA MINING AND RULE-BASED TECHNIQUES
Data mining and the use of rule-based techniques in random decision forest
Name of the Student
Name of the University
Author Note

1
DATA MINING AND RULE-BASED TECHNIQUES
Introduction
The prime determination of this unit of the paper is to focus on the importance of data
mining and the use of rule-based techniques in random decision forest.
Data mining is defined as the examination of the large databases which are
maintained by every business organization which deal with both structured and unstructured
data (Larose and Larose 2014). The prime objective of data mining is to generate new
information from the data sets.
A rule-based system is defined as the way to manipulate and store knowledge to interpret
information in a more useful way. This kind of systems is often used in the intelligent bots of
artificial intelligence. This system is very much useful in random decision forests as it has
plenty of categories incorporated into their body.
Discussion
The data mining technology is very much used for discovering the patterns of the
large datasets which are managed by the bots of the artificial intelligence. The working area
of data mining is around the intersection of the machine learning, database systems and
statistics. The intelligent methods applied by the artificial intelligent bots use the data mining
techniques for the purpose of extract information from a larger dataset (Thuraisingham 2014).
The data sets are extracted by the bots and it helps in transforming the unstructured data into
a structured form. Database management aspects are the other vital function of the data
mining techniques. The preprocessing of the data is also managed with the help of the data
mining techniques. The control of the interference considering the external security threats is
managed by the bots which work on the principals of the data mining (Witten et al. 2016).