Data mining is the process of finding patterns from large data sets and analyzing data from different perspectives. It allows business users to analyze data from different angles and summarize the relationships identified. Data mining can be useful in increasing the revenue and cut costs.
What is Data mining with an Example:
Assume a bank Bank of the World has multiple target segments . One of the target segments, female customers aged 18-24, needs to be analyzed. We find the account types they have and the average balances across these account types. We would also analyze the days when they walk into the bank/ATM or use online banking or the banking application more often. On preparing reports with many different parameters, the business users can summarize data such as Tuesdays are the least banked days or most women 18-24 years have large balances in checking account but considerably smaller investment balances. This analysis can help to optimize workforce on Tuesdays or offer sales pitches for investment accounts.
Data mining process:
Data mining analyzes relationships and patterns in the stored data based on user queries. Data mining involves four tasks.
- Association: Find the relationship between the variables. For example in a retail bank, we can determine which products are bought together frequently and this information can be used to market these products combined together with discounts.
- Clustering: Identifying the logical relationship in the data items and grouping them. For example in a retail store, a computer and a work desk can be logically grouped.
- Classifying: Involves in applying a known pattern to the new data.