Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. It is the analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.
Data mining can include the use of several types of software packages including analytics tools. It can be automated, or it can be largely labor-intensive, where individual workers send specific queries for information to an archive or database.
Generally, data mining defines operations that contain relatively sophisticated search operations that return focused and definite results. For instance, a data mining tool can view through dozens of years of accounting data to find a definite column of expenses or accounts receivable for a specific operating year.
Big Data refers to the vast amount that can be structured, semi-structured, and unstructured sets of data ranging in terms of tera-bytes. It is complex to process a large amount of data on an individual system that's why the RAM of this computer saves the interim computation during the processing and analyzing. When we try to process such a huge amount of data, it takes much time to do these processing steps on a single system. Also, our computer system doesn't work correctly due to overload.
Big data sets are those that outgrow the simple type of database and data handling structure that were used in previous times when big data was more highly-priced and less feasible. For instance, sets of data that are too high to be simply handled in a Microsoft Excel spreadsheet can be defined as big data sets.
Let us see the comparison between Data Mining and Big Data.
|Data Mining||Big Data|
|Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques.||Big Data is an all-inclusive term that defines the collection and subsequent analysis of significantly huge data sets that can include hidden data or insights that could not be found using traditional methods and tools. The amount of data is quite a lot for traditional computing systems to handle and analyze.|
|The purpose is to find patterns, anomalies, and correlations in a large store of data.||The purpose is to discover insights from data sets that are diverse, complex, and of massive scale.|
|Use cases include financial services, airlines and trucking companies, the healthcare sector, telecommunications and utilities, media and entertainment, e-commerce, education, IoT, etc.||It acts as a base to machine learning and artificial intelligence applications worldwide.|