Data mining is the process of sorting through large data sets to identify patterns and
establish relationships to solve problems through data analysis. Data mining tools
allow enterprises to predict future trends.
A data set is a collection of related, discrete items of related data that may be
accessed individually or in combination or managed as a whole entity.
Data mining parameters
In data mining, association rules are created by analyzing data for frequent if/then patterns,
then using the support and confidence criteria to locate the most important relationships
within the data. Support is how frequently the items appear in the database, while confidence
is the number of times if/then statements are accurate.
Association rules are if/then statements that help uncover relationships between
seemingly unrelated data in a relational database or other information repository. An
example of an association rule would be "If a customer buys a dozen eggs, he is
80% likely to also purchase milk."
An association rule has two parts, an antecedent (if) and a consequent (then). An
antecedent is an item found in the data. A consequent is an item that is found in
combination with the antecedent.
Association rules are created by analyzing data for frequent if/then patterns and
using the criteria support and confidence to identify the most important
relationships. Support is an indication of how frequently the items appear in the
database. Confidence indicates the number of times the if/then statements have
been found to be true.
In data mining, association rules are useful for analyzing and predicting customer
behavior. They play an important part in shopping basket data analysis, product
clustering, catalog design and store layout.
Programmers use association rules to build programs capable of machine learning.
Machine learning is a type of artificial intelligence (AI) that seeks to build programs
with the ability to become more efficient without being explicitly programmed.
Other data mining parameters include Sequence or Path Analysis, Classification, Clustering
and Forecasting. Sequence or Path Analysis parameters look for patterns where one event
leads to another later event. A Sequence is an ordered list of sets of items, and it is a common
type of data structure found in many databases. A Classification parameter looks for new
