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STUDENT ONE:

The process of extracting valid, unknown information from large databases is known as Data Mining. It is analysis of data and the use of different software techniques for extracting hidden relationships in sets of data. Cluster analysis is the type of technique that is used to group similar objects. Clusters are represented in different in many ways like trees. Performing clustering makes it easier to go through groups and easily find the trees. The main key points are data simplification and relationship identification. To know about data mining and clustering analysis gives the ability to discover the basic points related to the data that was discovered. The database is the main storage location to store new and old data. Having the ability to store information in a data warehouse gives the ability extract it in different ways. However, for small databases we extract information using queries, but as the amount and the data grows, it becomes very difficult. For extracting large amounts of data mining is one of the best ways. My job working for Coca Cola bottling company would keep certain data in certain software’s, but a lot of data is held in one software. This would be used to help us look for serial numbers, orders of equipment, and details of the purchase with the account number and pick slip.

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STUDENT TWO:

The lift value in an association rule is “is defined as the ratio of confidence to expected confidence.” (Evans, 2016, p. 319). It is the measure of the asset of an effect. The expected confidence of a rule is defined as “the product of the support values of the ruling body and the rule head divided by the support of the ruling body” (IBM, 2015). The Lift value is an important rule to interpret, and it is the measure of the rule displayed in the visualizer. An example of the lift value that is taken from my past workplace would be the ordering and purchasing of materials in the US NAVY. Let’s take the ordering and purchasing of toilet paper on a US Aircraft carrier. The good of the toilet paper would be 50%, where the expected confidence is 10% that a customer will purchase their toilet paper. So, the lift of the toilet paper goods rule is 5% (50%/10%=5%). The relationship between the toilet paper and goods is to find the substantial lift. The association is based on the capacity of the database system where it takes the numbers that are used in the scenario and are divided among themselves.