Page 168 - DCAP606_BUSINESS_INTELLIGENCE

Unit 11: Data Mining

11.9 Summary Notes

Data are any facts, numbers, or text that can be processed by a computer. Today,
organizations are accumulating vast and growing amounts of data in different formats
and different databases.
Data mining is the practice of automatically searching large stores of data to discover
patterns and trends that go beyond simple analysis.
Two widespread data mining methods for finding concealed patterns in data are clustering
and classification analysis.
A common approach for classifiers is to use decisions trees to partition and segment
records.

Data mining is utilised for a variety of reasons in both the personal and public parts.
One of the key matters raised by data mining technology is not an enterprise or
technological one, but a social one.

Data Mining is a relatively new concept that has not completely matured. Regardless of
this, there are a number of industries that are already using it on a normal basis.

The concerns about the individual privacy have been increasing enormously recently
particularly when internet is booming with social networks, e-commerce, forums, blogs
etc.

A data mining algorithm is a set of heuristics and calculations that creates a data mining
model from data. The algorithm first analyses the data you provide, looking for specific
types of patterns or trends and then creates the data.
11.10 Keywords

Association algorithms: This type of algorithm finds correlations between different attributes
in a dataset.

Association models: Association models are used to find and characterize co-occurrences.
Classification algorithms: This type of algorithm predicts one or more discrete variables, based
on the other attributes in the dataset.

Data: Data are any facts, numbers, or text that can be processed by a computer.
Data mining: Data mining is the practice of automatically searching large stores of data to
discover patterns and trends that go beyond simple analysis.

Data Mining Algorithm: A data mining algorithm is a set of heuristics and calculations that
creates a data mining model from data.
Network models: This type of model represents data by nodes and links.

Predictive models: These types of models predict how likely an event is to occur.
Regression: It is a statistical technique for estimating the relationships among variables.
Regression algorithms: This type of algorithm predicts one or more continuous variables.
Sequence analysis algorithms: This type summarizes frequent sequences or episodes in data.
Summary models: These models are used to summarize data.

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