Data mining refers to extraction of data or information from various online/offline resources to figure out specific patterns and establish relationships.
Data mining techniques are commonly used in a category of research areas such as marketing research, genetics, cybernetics and business intelligence. Text mining, Web mining and Meta research are widely used in customer relationship management (CRM) using the large amount of data collected by a Web portal to identify user behavior patterns.
Industry popular Data mining methods include:
Clustering method involves creation of different data clusters depending on the closeness or relationship among data and are forming a theme. For example an e-commerce website may render data clusters depending on region, gender, demography or even purchasing power.
Classification method refers to formation of data groups by applying known algorithm to the data warehouse under examination. The method is useful for business process which requires categorical information such as marketing or sales. It can use various algorithms such as closest neighbor, decision tree learning and others.
Regression technique employs mathematical formulas and is great for business which requires numerical information such as e-commerce and education. Regression method in essence looks at the numerical information and then tries to relate a formula that best fits that data.
Association technique is the widely used data mining method and leads to the invention of interesting relationships between variables as found in the data warehouse under examination. The data miner establishes a formula called association rule". He then predicts a future model and act upon the model to derive important information. For example take a case of academic degrees for specialization. If a student opt-in for a particular course then there may be a high probability that he may also choose relevant specialization in future to boost his career opportunity.
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