Data Extraction vs Data Mining
Data extraction and data mining are two data manipulating processes that are often interchanged and confused with one another. However, these two terminologies are different and are used for different purposes. This article will define and present the difference between the two terms.
Data extraction can simply be defined as the process of collecting data from various sources or in different formats into a single storage location and format, which can be used for further investigation or processing. However, data extraction techniques do not involve data processing. In most cases, data extraction is utilized to rearrange or organize badly structured data. The type of data that is extracted may come in the form of PDF documents, emails, web pages, and much more. Physical or cloud-based storage units are used to store data that has been extracted using data extraction techniques. Most data extraction algorithms are deployed online to collect data. Data extraction is sometimes referred to as data harvesting or data scraping.
Data extraction tools are usually deployed to collect data from one or several online sources. Within the data extraction algorithm, the type of data to be extracted can be defined. By defining the data type, the data extraction tool can extract the type of data required by the user. For example, a user that wants to extract specific types of videos from a particular website can limit the extraction process to such types of videos. Automatic bots are mostly utilized to perform data extraction. Most social media giants such as Facebook and Twitter make use of data mining tools.
Data mining is a data processing activity that is used to evaluate and analyze chunks of data using mathematical and statistical tools to find patterns and relations, which can be used to draw valuable conclusions. A well-developed data mining tool can effectively analyze an enormous amount of data to provide users valuable information. Businesses, as well as academic institutions, rely heavily on data mining to make decisions and evaluate academic results. In many cases, data extraction is instrumental in the completion of data mining processes as data must be collected before it is analyzed.
Data mining techniques are mostly complex mathematical processes that are implemented using high-performance computers. The data that has been mined can not only help researchers and entrepreneurs to draw conclusions, but it can help them make future predictions, which are invaluable. A well-designed data mining program can make projections together with probabilities that would give the user a better chance of making the right decision. The best part of most data mining techniques is that they are automatic. Once the program is set in motion, little to no human interaction is required. As a result, the outcome of data mining processes is fairly accurate, and reliable.
Most data mining programs can classify data as similar data sets. However, this is dependent on how the programmer designs the algorithm. As a result, most data mining processes are application-specific.
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