Exploration of Various Data Mining Topics (Research Paper Sample)

Data Mining
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How data and classification of data influence data mining
Data mining is a technique that turns raw data into meaningful information and incorporates techniques such as statistics, machine learning, and database systems. Data is the key ingredient of the data mining process; therefore, without data, the process of data mining is obsolete. The mining process entails navigating through the database, refining and extracting data which is then arranged into a meaningful structure based on common features.
The success of data mining depends on various factors, including the data source, completeness of the data, the applicability of big data analysis, customer engagement, and the business (Fedak, 2018). The aspect of data is part of the critical factors that influence the success of data mining. The effectiveness of data mining depends on the credibility of the data source. In addition, the data has to be complete connoting that, at a minimum, the essential characteristics and metrics are covered by at least one data source.
Classification assigns objects in a dataset or a collection of objects to target classes (Tan et al., 2016). Classification has a motive of precisely predicting the target class for every case in the data, such as identifying loan applicants as high, medium, or low credit risks. Classification allows organizations to answer paramount questions. One objective of data mining is dependency modeling, i.e., identifying existing relationships within a dataset. Suppose when conducting classification, the data were grouped into the wrong group, the credibility of data mining would be undermined as the technique would provide relationships based on wrong