What is Data Mining ?

I’m sure you’ve heard the phrase “Data Miner” in relation to IT and have probably been confused by it. Don’t get me wrong, I was originally lost as well and thought it just meant someone who dug for information via mining. I went with that definition for a very long time until my confusion prompted me to ask an IT specialist what data mining is. This was his response: Data mining is the process of analyzing data from various sources to extract useful information. The goal of this technology is to organize large amounts of data into usable data sets, so businesses can easily locate trends or opportunities. Data mining helps identify trends, patterns and other interesting relationships between market segments. This helps businesses understand customer needs and optimize the overall marketing process.

Data Mining Techniques:

There are many different types of data mining techniques, but they all fall into one of three categories: association rules, classification and clustering.

Association rules are used to identify which items tend to occur together in a given dataset. For example, if you were looking at grocery store sales data, you could use association rules to see that people who buy soda also tend to buy chips or snacks. This type of analysis can help companies determine what products would be most beneficial for them to stock in their stores or online catalogs.

Classification is used to assign a label or category to each item in a dataset based on pre-defined characteristics or attributes. For example, if you were analyzing customer email addresses, you could group all emails ending in “.com” into one category and those ending in “.edu” into another category based on what domain name they were registered under (such as Yahoo! Mail or Gmail). This type of analysis helps businesses determine

Data mining can help your Business By

• Identifying customer buying patterns

• Understanding customer preferences

• Recognizing customer segments (including those who are likely to churn)

• Segmenting customers based on demographic information such as age or gender

• Identifying high-value customers (those who are profitable)

• Creating new products based on customer feedback

Data Mining Steps:

Data Preparation: The first step in data mining is to prepare the data for analysis by cleaning it up and transforming it into a form that can be analyzed.

Data Reduction: In this step, the large amount of raw data is reduced to a smaller set of more manageable results. This reduction process is often referred to as “dimensionality reduction” because it reduces the number of dimensions or axes on which the data is plotted or analyzed. For example, a company may have hundreds of thousands of customer records but only want to analyze those customers who live in certain cities or states. The dimensionality reduction component will eliminate all records except those belonging to customers who live in those cities or states.

Pattern Detection: In this step, algorithms are used to detect patterns in the reduced data set which can then be used for further analysis or acted upon directly by computers using rules derived from those patterns (for example, applying credit limits based on past spending patterns). This pattern detection component

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Bill-bradley. (CEO)

What is Data Mining and How Can I Use It For My Business