This tip from Han and Kamber's book Data Mining Concepts and Techniques (Morgan Kaufmann) examines the components of a data mining query.
Each user will have a data mining task in mind, that is, some
- Task-relevant data: This is the database portion to be investigated. For example, suppose that you are a manager of AllElectronics in charge of sales in the United States and Canada. In particular, you would like to study the buying trends of customers in Canada. Rather than mining the entire database, you can specify that only the data relating to the customer purchases in Canada need be retrieved, along with the related customer profile information. You can also specify attributes of interest to be considered in the mining process. These are referred to as relevant attributes. For example, if you are interested only in studying possible relationships between, say, the items purchased and customer annual income and age, then the attributes name of the relation item, and income and age of the relation customer, can be specified as the relevant attributes for mining.
- The kinds of knowledge to be mined: This specifies the data mining functions to be performed, such as characterization, discrimination, association, classification, clustering, or evolution analysis. For instance, if studying the buying habits of customers in Canada, you may choose to mine associations between customer profiles and the items that these customers like to buy.
- Background knowledge: Users can specify background knowledge, or knowledge about the domain to be mined. This knowledge is useful for guiding the knowledge discovery process and for evaluating the patterns found. There are several kinds of background knowledge. One popular form of background knowledge is known as concept hierarchies. Concept hierarchies are useful in that they allow data to be mined at multiple levels of abstraction. Other examples include user beliefs regarding relationships in the data. These can be used to evaluate the discovered patterns according to their degree of unexpectedness (where unexpected patterns are deemed interesting) or expectedness (where patterns that confirm a user hypothesis are considered interesting).
- "Interestingness" measures: These functions are used to separate uninteresting patterns from knowledge. They may be used to guide the mining process or, after discovery, to evaluate the discovered patterns. Different kinds of knowledge may have different interestingness measures. For example, interestingness measures for association rules include support (the percentage of task-relevant data tuples for which the rule pattern appears) and confidence (an estimate of the strength of the implication of the rule). Rules whose support and confidence values are below user-specified thresholds are considered uninteresting.
- Present and visualization of discovered patterns: This refers to the form in which discovered patterns are to be displayed. Users can choose from different forms for knowledge presentation, such as rules, tables, charts, graphs, decision trees, and cubes.
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This was first published in June 2001