First initial discussion post Create an initial post in which you analyze multip
ID: 3847541 • Letter: F
Question
First initial discussion post Create an initial post in which you analyze multiple data mining types. Address the following: Briefly summarize each type of data mining discussed in the textbook. Identify examples of databases that could be data mined using each type of data mining o Identify what patterns are likely to appear in each example. Explain why these patterns might appear. Based on the patterns you might find in your examples; explain what sort of information you might be able to infer from your data mining. Fully state and justify any choices, assumptions or claims that you make using the suggested Learning Resources for this Week and/or your own research. Support your explanations with specific software development examples.Explanation / Answer
first of all we will know what is data mining?
Data mining is defined as the computer process that analyzes sets of data i.e large in size and then extracting the information out of it. data mining automates the detections of relevant patterns in the database. data mining can be used in weather forecasting,product designing etc.
types of Data Mining are as follows:
lets discuss on above types of data mining
1.Association-
we can easily understand the meaning of association i.e.relation between two items. this type is simple and clear. for example, when we shop online we select multiple products we like, and we close the website after completing the order. again when er come next time and visit the catalogs on the website, it shows us the products you maya like. website is understanding your needs and displaying the products associated with the other products you bought previously. Therefore this type is just common relationship between the items or data.
2.Classification:
The type simply shows that there is some partition in data or you can say data are classified into various groups. for example, take your mail account, there you can find inbox,spam,sent items etc. these are groups in which all data are partitioned.
3.Clustering:
At simpe point clustering means joining various items into one unit. but its not like classification. clustering does not have dedicated labels. In clustering whether the objects know each other in a group or there is no association between them.
4.Sequential Patterns:
Sequential patterns seeks to discover or identify similar patterns, regular events in transaction data over a period. the patterns occurs frequently oer a period of time or events. for eg, medical checkups
5.Decision trees:
In this type, there is a root node which is parent and the subnodes which are child nodes. the decision is taken on the basis of the child nodes.
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