DATA data. In other words, it identifies useful



















Dexter Gose

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– Database Fundamentals


10, 2017





of Data mining

Data mining,
also referred to as Knowledge Discovery from Data (KDD), denotes a process used
to extract knowledge or patterns from an array of data. These materials are
sourced from data warehouses, databases, the Internet, and other repositories.

data mining is applicable to almost every type of data provided that they are
deemed significant for a specific application. These data forms utilized for
mining applications are not only limited to database, data warehouse, and
transactions, but also spatial, text, sequence, multimedia, data streams, graph
or networked data, as well as the World Wide Web.

Data mining uses complex mathematical
algorithms to categorize data and determine the likelihood of forthcoming
events. This whole process involves a number of
stages which commences from the extraction and management of data up to
analysis, definition, and graph representation. Initially, the data are
extracted, transformed, and loaded as transaction data onto the data warehouse.
Once this is completed, they are stored and managed in a multi-dimensional
database system.  Analysts and
specialists are then provided access to these data which are represented in a suitable
layout such as in the form of a table or a graph.


Importance of Data mining

In this modern age, the importance of data
mining cannot be denied due to its various benefits. Data mining applies software
programs to categorize various patterns and relationships in a series of data.
In other words, it identifies useful pieces of data which are relevant to a
number of areas.

These extracted information enable businesses to come up with knowledge-driven
results for numerous aspects such as advertising, marketing,
and the introduction of new of products and
services, among others. Owing to the application of these data mining
tools, businesses can now easily provide resolutions to queries which were
previously considered as time-consuming.

Data mining is also of great importance in data pre-processing,
data cleaning, as well as database integration. For instance, researchers can
now easily determine similar data patterns, identify co-existing sequences, aside
from finding out associations between a series of research activities. Consequently,
these bring groundbreaking
changes to the field of research, as data mining presents more comprehensible
data forms.

Data mining
Implementation and Software




Data mining
in Industry

The process of datamining is primarily utilized
these days by a number of industries including those related to marketing,
retail, finance, and communication, among other fields.

One of these
industries is healthcare or medical science in which the future direction of a
health care system is analyzed through data mining tools. Healthcare
providers make use of data mining to determine the most efficient treatments
and practices by making a comparison of several causes, symptoms, treatments
and effects prior to analysing which action will demonstrate the utmost
effectiveness for the patients.


Data mining also plays a pivotal role in numerous
areas of manufacturing through the discovery of useful patterns in complex
manufacturing processes with the use of data mining tools. It is also utilized
to establish control parameters that allow optimum production to be accurately
reproduced from one factory to another, while also enabling manufacturers to
detect any faulty equipment during the process.

Furthermore, data mining has also proven its
significance in the field of finance or banking through the use of customer
data in order to render better credit decisions, as well as to identify
possibly fraudulent activities. For example, a financial institution can assess
both good and bad loans, and protect credit card owners when transactions are

Likewise, it also provides an identical role
to the government by looking into citizens’ financial records which can be applied
to determine potential criminal activities like money laundering. The large number of crime datasets, together with the intricacy
of relations between these data forms have led to criminology as a suitable
field to make use of the different techniques of data mining.































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