Humans have been "manually" extracting patterns from data for
centuries, but the increasing volume of data in modern times has
called for more automatic approaches. Early methods of identifying
patterns in data include Bayes' theorem (1700s) and Regression
analysis (1800s). The proliferation, ubiquity and incre- ing power
of computer technology has increased data collection and storage.
As data sets have grown in size and complexity, direct hands-on
data analysis has - creasingly been augmented with indirect,
automatic data processing. Data mining has been developed as the
tool for extracting hidden patterns from data, by using computing
power and applying new techniques and methodologies for knowledge
discovery. This has been aided by other discoveries in computer
science, such as Neural networks, Clustering, Genetic algorithms
(1950s), Decision trees (1960s) and Support vector machines
(1980s). Data mining commonlyinvolves four classes of tasks: *
Classi cation: Arranges the data into prede ned groups. For
example, an e-mail program might attempt to classify an e-mail as
legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive
Bayes classi er and Neural network. * Clustering: Is like classi
cation but the groups are not prede ned, so the algorithm will try
to group similar items together. * Regression: Attempts to nd a
function which models the data with the least error. A common
method is to use Genetic Programming. * Association rule learning:
Searches for relationships between variables. For example, a
supermarket might gather data of what each customer buys.
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