Data Warehousing and Mining Essay Example | Topics and Well Written Essays - 4500 words

Data Warehousing and Mining - Essay ExampleThis paper go forth discuss the concept of information mining in detail. This paper will discuss the main aspects, techniques and algorithms of data mining. This paper will also assess the grocery applications of data mining. DATA MINING Data mining is a technique which is used to evaluate business or corporate data from a quarry source and after that turn that data into valuable and useful information. This corporate information is normally employed to facilitate a business to raise profits, condense cut expenditure in specific business areas. Moreover, the main purpose of data mining applications is to recognize and take-out similar business configuration enclosed in a given club of corporate data (Bradford, 2011). IMPORTANT DATA MINING TECHNIQUES This section outlines some of the prime and important data mining techniques. rough of the main techniques are presented below Neural Networks/Pattern Recognition Neural Networks are utili zed in a blackbox style. In this technique, an individual produces a set of data for testing purpose, which allows the neural network to find out patterns based on the identified results, then for these data permits the neural network imprecise on massive amounts of data provided. ... Memory Based Reasoning This technique can offer same results which can be achieved from neural network however the works of this technique is different from neural networks. In addition, the memory based reasoning searches for closely related type of data, rather than considering similar working patterns (Chicago Business experience Group, 2011) and (Han & Kamber, 2006). Cluster Detection This is a standard technique of data mining which is used to assess the relationship between market and business transaction data because it discovers associations from data patterns. Mainly, this method discovers associations in clients or product or anywhere we desire to discover interaction in data (Chicago Busine ss newsworthiness Group, 2011) and (Han & Kamber, 2006). crosstie Analysis This is another method for relating similar business records. However, this method is not utilized extensively on the other hand, a number of methods and software applications need been built on the basis of this technique. Since its name states, this technique attempts to discover associations, either in dealings, various products, consumers, etc. as well as reveals those associations (Chicago Business Intelligence Group, 2011) and (Han & Kamber, 2006). Visualization This method of data mining facilitates the users to recognize their data. In this scenario, visualization is used to create the association from text established to visual/graphical arrangement. In addition, various other techniques such as rule, decision tree, pattern visualization and cluster facilitate users to observe data associations rather than reading the associations. Moreover, a volume of powerful data mining systems have taken eff ective actions for enhancing their