describe data cleaning and data dredging

What are the major issues of data mining? Mining methodology and user interaction issues Data Warehouses, Transactional Databases, Relational Databases, Multimedia Databases, Spatial Databases, Time-series Databases, and World Wide Web.

Purposes of data miningĭata mining can be applied to any type of data e.g. In the context of computer science, Data Mining can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. The process of extracting information to identify patterns, trends, and useful data that would allow the business to take data-driven decisions from huge sets of data is called data mining. In this article, we will understand the purpose of data mining and the various issues that take place with it. Therefore, the selection of the right data mining tools is a very challenging task. Different data mining instruments operate in distinct ways due to the different algorithms used in their design.

More difficulties get uncovered as the genuine data mining measure begins, and the achievement of data mining lies in defeating every one of these difficulties. Data mining (one of the core disciplines of data science) helps in data extraction but there are many difficulties in data mining.

Describe data cleaning and data dredging