**4. Discussions**

#### **4.1 The cloud and data mining's future**

The development of data mining has been accelerated by cloud computing technology. Cloud systems are ideally adapted for today's high-speed, massive amounts of semi-structured and unstructured data that most businesses must contend with. The elastic capabilities of the cloud will easily scale to meet these big data demands. As a result, since the cloud can carry more data in a variety of formats, more data mining techniques are needed to transform the data into insight. Advanced data mining techniques such as AI and deep learning are now available as cloud services.

Future advancements in cloud computing would undoubtedly increase the need for more powerful data mining software. AI and machine learning will become even more commonplace in the next five years than they are now. The cloud is the most suitable way to both store and process data for business value, given the exponentially growing pace of data growth on a daily basis. As a result, data mining methods can depend much more on the cloud than they do now.

Currently, data scientists use a variety of data mining techniques, which differ in precision, efficiency, and the type and/or volume of data available for analysis. Classical and modern data mining techniques are two types of data mining techniques. Statistical approaches, Nearest Neighbors, Clustering, and Regression Analysis are examples of Classical techniques, while Modern techniques include Neural Networks, Rule Induction Systems, and Decision Trees.
