What is Data Mining?
Data mining helps organization having large sized
database to explore their data in a simple and interactive way. It is used to find
patterns of the data and performs predictions on the process like a transaction
on the server will succeed or not. It provides real time predictions and its
technologies, help users to analyze data as well as discovers hidden patterns in
their data sets. Data mining uses combination of the statistics, probability,
artificial intelligence, machine learning and database technologies etc to
analyze particular data sets.
We can call data mining as the process of exploring
data from large sized databases and extracting required information according to
certain rules and patterns. Different analysis schemes are used in order to
recognize the patterns or the rules in the historical data according to the
provided business scenario. The information thus can be stored in an abstract
mathematical model which is termed as the Data
Mining Model. Once this is done, new
database is examined using this model to add relevant information according to
the rules or patterns. This is done in accordance to improve results for a
query for a given business scenario.
Understanding with an Example: With analysis of recorded
database for the number of its items that are purchased from different shops,
retail stores, or supermarket chains, it is possible to derive information
about the product that are sold most so that its supply can be increased
accordingly. In short, Data Mining is an analytical activity where hidden
patterns are studied by sorting huge sized database.
Why Consider
Data Mining: There are many advantages that help users to choose data mining
techniques. But few are listed below:
- It helps to discover reasons for success and failure.
- It helps to understand your customers, products etc.
- It improves your organization by mining large sized databases.
Know What SQL Data Mining is?
SQL Data
Mining is
an automatic backend procedure where a set of machine learning algorithms explores
the database for the defined patterns. Once designed, these patterns can be a
great help to get a better insight to the data and then can be further used for
creating predictions that allow exploring different facts based on the defined
algorithms.
There are nine mining algorithms for SQL Server and
additional tools are required for creating and deploying the data mining models
that suits situation of a business. For SQL data mining, free Business
Intelligence Development Studio (BIDS) is available free by the Microsoft.
Mathematical techniques are applied on a set of data called mining algorithms.
.NET framework, BDIS, DMX languages are used as custom Microsoft solutions and
this is the reason why data mining is sometimes referred to as machine
learning.
SQL Data Mining Algorithms
Data Mining algorithms is a heuristic program that
creates a data mining model from the warehouse. The algorithms first examines
the provided data, search out for particular rules and patterns. The outcome of
this analysis is used by the algorithms to create data mining model (DMM). The
parameters defined for DMM are applied to the data warehouse for extracting
detailed statistics. The mining model can be converted into:
- Set of clusters illustrating how to relate the cases in dataset.
- Decision Tree forecasts about the outcome and its after-effects.
- Set of Rules explain how to group the products in a transaction.
SQL Server Analysis Services (SAAS) provides variety
of algorithms for a perfect data mining solution. All these algorithms are
customizable.
Classification Algorithms: It predicts distinct
variables depending upon the different attributes in the dataset.
Regression Algorithms: It creates a linear
equation for different type of variables so that is most suitable for the
dataset.
Segmentation Algorithms: This helps to categorize
the database into certain groups or clusters that share similar properties.
Association Algorithms: It helps to relate
different attributes of the database. This is one of the widely accepted
algorithms used for market based analysis.
Sequence Analysis Algorithms: This helps to explore the
data that is linked by sequences. For Example: Web Path Flow
One or more algorithms can
be adopted as a part of solution for businesses. Experienced analysts adopt
only one algorithm to test output after an input and then apply another
algorithm to test outcome based on provided data.
Conclusion:
Data mining techniques
are helpful for those users who deal with large sized database, analyse event failure and predict about the data etc.
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