Data Mining and Rule-Based Techniques in Random Decision Forest

This paper discusses the importance of data mining and the use of rule-based techniques in random decision forest. It also highlights the impact of data mining technology in artificial intelligence and how rule-based techniques can be useful in analyzing decision forests.

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

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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).

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DATA MINING AND RULE-BASED TECHNIQUES
The complexity consideration of the larger datasets is effectively managed with the help of
the data mining techniques.
Manipulation of the data to interpret the information in a more structured way is the
prime characteristic feature of the rule-based techniques, this characteristic feature this
applied in the random decision forests as those forests have a very complicated structure. All
the individual trees of the decision forest can be properly studied with the help of the rule-
based systems. This machine learning tasks can be performed effectively with the help of the
rule-based systems. The randomized trees of the decision forest can be carefully categorized
and evaluated if the rule-based techniques are used. The evaluation consists of the decision
forest includes the structured examination of each node of an individual tree (Yuan, Zhang
and Liu 2015). The neighboring trees which are some connected to the trees can be evaluated
with the help of this rule based technique as the other evaluation methods are very much
complicated and consider the total forest. The rule-based system is more beneficial for the
evaluation of the decision forests as the other techniques do not individually examine the
trees.
Conclusion
From the above paper, the impact of the data mining technology in the artificial
intelligence as the automated technologies are extensively used in the preservation of the
large datasets in the global organizations which deal with both structured and unstructured
data. It can be also concluded that the classification problems such as the random decision
forests can be very much useful for the rule-based technique as the different sentiments of the
decision forests can be better analyzed with the help of this method.

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DATA MINING AND RULE-BASED TECHNIQUES
Reference
Larose, D.T. and Larose, C.D., 2014. Discovering knowledge in data: an introduction to data
mining. John Wiley & Sons.
Thuraisingham, B., 2014. Data mining: technologies, techniques, tools, and trends. CRC
press.
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Yuan, C., Zhang, Y. and Liu, Z., 2015. A survey on technologies for automatic forest fire
monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing
techniques. Canadian journal of forest research, 45(7), pp.783-792.