Difference Between Data Mining and Query Tools
Data mining and OLAP are two of the Business Intelligence (BI) technologies. Business intelligence refers to computational methods to identify and extract useful information from business data. Data mining is the field of computer science that makes the business of extracting interesting designs large sets of data. It combines many methods of artificial intelligence, statistics and management of databases. OLAP (online analytical processing) as the name suggests is a collection of ways to query the multidimensional databases.
Data mining is also known as Knowledge Discovery in Data (KDD). As stated above, is a field of computer science, which deals with the extraction of previously unknown and interesting information from raw data. Because of the exponential growth of data, especially in business, data mining has become an important instrument in converting this wealth of data to business intelligence. Manual extraction of patterns that was nearly impossible till a few decades ago is now easier via data mining. For example, it is currently used for different applications such as fraud detection, social network analysis and marketing. Data mining usually takes care of four tasks: classification, clustering, regression and the association. Clustering identifies similar groups of unstructured data. Learning rules and applying them to new data is called classification. It essentially includes the following: preprocessing of data, designing of models, feature selection/learning and validation/evaluation. To find functions with minimum error to model data is called regression. The association looks out for relationships among variables. Data mining is provides answers to questions like what are the main products that could help achieve high profit next year in Wal-mart.
OLAP is a class of systems that provide answers to multidimensional queries. Characteristically OLAP is used for marketing, budgeting, and providing similar applications. It goes without saying that the databases used for OLAP are configured for ad hoc and complex queries with fast performance in mind. Generally a matrix is used to display the output of OLAP. Rows and columns are formed by the dimensions of the query. They often use methods for the aggregation of multiple tables for summaries. For example, it can be used to compare sales in Wal-Mart between the previous year and current year. What is the prediction of sales in the next year? What can be said of the trend by looking at the percentage change?
Although it is clear that both OLAP and data mining are similar since they operate on the data to gain intelligence, the main difference comes from how they operate on the data. OLAP tools provide multidimensional data analysis and provide summaries of data, however, the focus of data mining is on the ratios, patterns and influence in the set of data. An OLAP is all about aggregation, which comes down to the operation of data via ‘addition’ but data mining is about ‘ division’. On the other notable difference is that while the data mining tools and model data and return actionable rules, OLAP perform the comparison and contrast techniques along the dimension business real-time.