Data Mining Task Primitives
A data mining task can be specified in the form of a data mining query, which is input to the data mining system. A data mining query is defined in terms of data mining task primitives. These primitives allow the user to interactively communicate with the data mining system during discovery to direct the mining process or examine the findings from different angles or depths. The data mining primitives specify the following,
A data mining query language can be designed to incorporate these primitives, allowing users to interact with data mining systems flexibly. Having a data mining query language provides a foundation on which user-friendly graphical interfaces can be built.
Designing a comprehensive data mining language is challenging because data mining covers a wide spectrum of tasks, from data characterization to evolution analysis. Each task has different requirements. The design of an effective data mining query language requires a deep understanding of the power, limitation, and underlying mechanisms of the various kinds of data mining tasks. This facilitates a data mining system's communication with other information systems and integrates with the overall information processing environment.
List of Data Mining Task Primitives
A data mining query is defined in terms of the following primitives, such as:
1. The set of task-relevant data to be mined
This specifies the portions of the database or the set of data in which the user is interested. This includes the database attributes or data warehouse dimensions of interest (the relevant attributes or dimensions).
In a relational database, the set of task-relevant data can be collected via a relational query involving operations like selection, projection, join, and aggregation.
The data collection process results in a new data relational called the initial data relation. The initial data relation can be ordered or grouped according to the conditions specified in the query. This data retrieval can be thought of as a subtask of the data mining task.
This initial relation may or may not correspond to physical relation in the database. Since virtual relations are called Views in the field of databases, the set of task-relevant data for data mining is called a minable view.
2. The kind of knowledge to be mined
This specifies the data mining functions to be performed, such as characterization, discrimination, association or correlation analysis, classification, prediction, clustering, outlier analysis, or evolution analysis.
3. The background knowledge to be used in the discovery process
This knowledge about the domain to be mined is useful for guiding the knowledge discovery process and evaluating the patterns found. Concept hierarchies are a popular form of background knowledge, which allows data to be mined at multiple levels of abstraction.
Concept hierarchy defines a sequence of mappings from low-level concepts to higher-level, more general concepts.
An example of a concept hierarchy for the attribute (or dimension) age is shown below. User beliefs regarding relationships in the data are another form of background knowledge.
4. The interestingness measures and thresholds for pattern evaluation
Different kinds of knowledge may have different interesting measures. They may be used to guide the mining process or, after discovery, to evaluate the discovered patterns. For example, interesting measures for association rules include support and confidence. Rules whose support and confidence values are below user-specified thresholds are considered uninteresting.
5. The expected representation for visualizing the discovered patterns
This refers to the form in which discovered patterns are to be displayed, which may include rules, tables, cross tabs, charts, graphs, decision trees, cubes, or other visual representations.
Users must be able to specify the forms of presentation to be used for displaying the discovered patterns. Some representation forms may be better suited than others for particular kinds of knowledge.
For example, generalized relations and their corresponding cross tabs or pie/bar charts are good for presenting characteristic descriptions, whereas decision trees are common for classification.
Example of Data Mining Task Primitives
Suppose, as a marketing manager of AllElectronics, you would like to classify customers based on their buying patterns. You are especially interested in those customers whose salary is no less than $40,000 and who have bought more than $1,000 worth of items, each of which is priced at no less than $100.
In particular, you are interested in the customer's age, income, the types of items purchased, the purchase location, and where the items were made. You would like to view the resulting classification in the form of rules. This data mining query is expressed in DMQL3 as follows, where each line of the query has been enumerated to aid in our discussion.