Download Data Mining Primitives, Languages, and System

Data Mining Primitives, Languages, and System
Data Mining Primitives
 Task-relevant data
 The kinds of knowledge to be mined:
 Background Knowledge
 Interestingness measures
 Presentation and visualization of discovered Patters:
Task-relevant data:
the data base portion to be investigated.
database, you can specify the portion of the database that is
analysis/investigation.
E.g.: Transaction involving customer purchases is “Canda” need to be retrieved
The kinds of knowledge to be mined:
This specifies the data mining functions to be performed, such as
Background Knowledge:
Users can specify the background knowledge or knowledge about the domain to be
mined.
and for evaluating the patterns found.
There are several kinds of background knowledge such as:
Concept hierarchies: help in mining data at multiple levels of abstraction.
Beliefs regarding relationships in data: This helps to evaluate the discovered patterns
according to their degree of unexpectedness or expectedness.
Interestingness measures:

nterestingness measures are used o to separate uninteresting patterns from
knowledge.
 To guide the mining process.
 To evaluate the discovered patters.

Example: Association rule mining has interestingness measures, such as
Support : the % of task-relevant relevant data tuples for which the rulepattern appears.
Confidence: an estimate of the strength of the implication of the rule.
The rules whose support and confidence values are below user-specified threshold are
considered uninteresting.
Presentation and visualization of discovered Patters:
This refers to the form in which discovered patterns to be displayed