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Business Intelligence
Data Mining
Main Agenda
• Concepts and Application
• Techniques and tools
What is Data Mining
• Data mining concepts and techniques
aims at uncovering interesting data
patterns hidden in large data sets.
• The information and knowledge gained
from data mining can be used for
applications ranging from market analysis,
fraud detection, and customer retention, to
production control and science
exploration.
History of Data Mining
• Data mining can be viewed as a result of
the natural evolution of information
technology.
• The database system industry has
witnessed an evolutionary path in the
development of the following
functionalities.
– data collection and database creation, data
management (including data storage and
retrieval, and database transaction
processing)
History of Data Mining
– It also includes advanced data analysis
(involving data warehousing and data mining).
• The steady and amazing progress of
computer hardware technology in the past
three decades has led to large supplies of
powerful and affordable computers, data
collection equipment, and storage media.
History of Data Mining
• This technology provides a great boost to
the database and information industry, and
makes a huge number of databases and
information repositories available for
transaction management, information
retrieval, and data analysis.
• This led to the emergence of Data
Warehouse technology.
History of Data Mining
• Data warehouse technology such as online analytical processing (OLAP) focuses
on analysis techniques such as
summarization, consolidation, and
aggregation as well as the ability to view
information from different angles.
History of Data Mining
• Although OLAP tools support
multidimensional analysis and decision
making, additional data analysis tools are
required for in-depth analysis, such as
data classification and clustering.
• Also huge amounts of data is accumulated
beyond database and data warehouses
such as www and data streams.
– The effective and efficient analysis of data in
such different forms become challenging task
History of Data Mining
• The abundance of data, coupled with the
need for powerful data analysis tools, has
been described as a data rich but
information poor situation.
History of Data Mining
• We are data rich, but information poor
History of Data Mining
• All of the above reasons lead to the
invention of Data Mining.
• And the major focus of data mining tools is
to perform data analysis and may uncover
important data patterns, and contributing
greatly to business strategy and scientific
and medical research.
So, What Is Data Mining?
• Simply stated, data mining refers to
extracting or “mining” knowledge from
large amounts of data.
• The term is actually a misnomer.
• Remember that the mining of gold from
rocks or sand is referred to as gold mining
rather than rock or sand mining.
So, What Is Data Mining?
• Thus, data mining should have been more
appropriately named “knowledge mining
from data”, which is unfortunately
somewhat long.
• “Knowledge mining,” a shorter term, may
not reflect the emphasis on mining from
large amounts of data.
• Thus, such a misnomer that carries both
“data” and “mining” became a popular
choice.
So, What Is Data Mining?
• Many other terms carry a similar or slightly
different meaning to data mining, such as
knowledge mining from data,
knowledge extraction, pattern analysis,
data archaeology, and data dredging.
• Many people treat data mining as a
synonym for another popularly used term,
Knowledge Discovery from Data, or
KDD.
So, What Is Data Mining?
• Simply put (standard definition);
Data mining is the process of discovering
interesting knowledge from large amounts
of data stored in databases, data
warehouses, or other information
repositories.
Architecture of DM system
• Based on this definition, the architecture of
a typical data mining system may have the
following major components:– Database, data warehouse, World Wide
Web, or other information repository: This
is one or a set of databases, data
warehouses, spreadsheets, or other kinds of
information repositories. Data cleaning and
data integration techniques may be performed
on the data.
Architecture of DM system
– Database or data warehouse server: The
database or data warehouse server is
responsible for fetching the relevant data,
based on the user’s data mining request.
Data Mining Tasks
• Data Mining Tasks can be classified into
two categories:
– Descriptive
– Predictive.
• Descriptive mining tasks characterize the
general properties of the data in the
database.
• Predictive mining tasks perform inference
on the current data in order to make
predictions.
Concept/Class Description
• Data can be associated with classes or
concepts.
• For example, in the AllElectronics store,
classes of items for sale include
computers and printers, and concepts of
customers include bigSpenders and
budgetSpenders.