There are several major data mining techniques have been developed and used in data mining projects recently including Association, Classification, Clustering, Prediction and Sequential Patterns.
Data mining techniques are used in a many research areas, including mathematics, cybernetics, genetics and marketing.
Web mining, a type of data mining used in Customer Relationship Management (CRM), takes advantage of the huge amount of information gathered by a Web site to look for patterns in user behaviour.
Association
(Looking for patterns where one event is connected to another event)
Association is one of the best known data mining technique. In association, a pattern is discovered based on a relationship of a particular item on other items in the same transaction. For example, the association technique is used in market basket analysis to identify what products that customers frequently purchase together. Based on this data businesses can have corresponding marketing campaign to sell more products to make more profit.Classification
(Looking for new patterns)
Classification is a classic data mining technique based on machine learning. Basically classification is used to classify each item in a set of data into one of predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. In classification, we make the software that can learn how to classify the data items into groups. For example, we can apply classification in application that “given all past records of employees who left the company, predict which current employees are probably to leave in the future.” In this case, we divide the employee’s records into two groups that are “leave” and “stay”. And then we can ask our data mining software to classify the employees into each group.
Clustering
(Finding and visually documenting groups of facts not previously known)
Clustering is a data mining technique that makes meaningful or useful cluster of objects that have similar characteristic using automatic technique. Different from classification, clustering technique also defines the classes and put objects in them, while in classification objects are assigned into predefined classes. To make the concept clearer, we can take library as an example. In a library, books have a wide range of topics available. The challenge is how to keep those books in a way that readers can take several books in a specific topic without hassle. By using clustering technique, we can keep books that have some kind of similarities in one cluster or one shelf and label it with a meaningful name. If readers want to grab books in a topic, he or she would only go to that shelf instead of looking the whole in the whole library.
Prediction / Forecasting
(Discovering patterns in data that can lead to reasonable predictions about the future)
The prediction as it name implied is one of a data mining techniques that discovers relationship between independent variables and relationship between dependent and independent variables. For instance, prediction analysis technique can be used in sale to predict profit for the future if we consider sale is an independent variable, profit could be a dependent variable. Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction.Sequential Patterns
(Looking for patterns where one event leads to another later event)
Sequential patterns analysis in one of data mining technique that seeks to discover similar patterns in data transaction over a business period. The uncover patterns are used for further business analysis to recognize relationships among data.