Question

In a 1-2 page paper explain the difference in how you would mine data based on the 3 categories; Prediction, Clustering, and Association. Within the paper, please include responses to the following questions:

• What is the difference in the type of data needed?
• Which data mining approach would you choose? Why?
• How will the outcomes of the analysis be used?

Answer 1

Data mining is the process through which large bulk of data is analyzed in order to find new information. there are various techniques of data mining such as- prediction, clustering, association, tracking, classification etc.

1. data prediction- prediction as the name itself suggests is the anticipation about future. in this case generally past data is recognized and analyzed to predict the future trend.this method helps in deciding about the future behavior of a variable. for example- if we analyze the credit history of consumers in past then we can easily predict the future credit risk that we may face.

2. data clustering- cluster means group. so data clustering is the grouping of data based on similarity. this technique helps in identifying similar data and in turn helps us in decision making process. for example we can cluster data according to various demographic factors like age, gender, education etc. so that we can make the required decisions.

3. data association- in this case association or relation between various groups of data is identified. we try to find attributes that are highly correlated with other variable. for example there may be correlation between purchase of two items. in this case we can predict the demand of the commodity.

data is something which is available in raw form and needs to be processed so that some information can be made available for decision making . it may be available in different forms such as number, character, text, words , statements or sometimes it may be even in the form of special characters. data may be even in the form of pictures, sound videos, etc. so in simple words data may be in various forms that can be processed to gain some useful information.

as already discussed there are various data mining approach or techniques like tracking, classification. regression, clustering, prediction etc. each method has it's own features, advantages and disadvantages, so the use of various method depends of the requirement. for example if we have past data available with us then we can use data prediction method to anticipate about the future trend of the data. similarly if we want to identify the relation between two variables the we can use data regression method etc.

the various technique of data mining provides us useful information and helps us in predicting future trend. this in turn enables us to make decision about various variables. data mining is the process of extracting useful information from huge bulk of data, in order to use outcome of data mining we follow few steps such as first of all we identify data and convert it into another useful form and determine the nature of data. thereafter we identify the pattern in data to make future prediction about various variables and finally we get the desired outcome from the raw data and use it for the decision making process.