Data Mining.pdf

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The Data Mining process is the extraction of valid 
and previously unknown information.
Data warehouse is a subject oriented, 
integrated, time-variant, non-volatile collection 
of data in support of management decision-
making process. 
To handle bulk of data in various enterprises, thereby 
increasing the margin
To turn incomprehensible data into usable 
information
It is a combination of ideas from statistics, machine 
learning, databases and parallel computing
As it has both hardware & soft ware components, which facilitates in 
taking better decisions in massive companies
It is also an architecture & tool to collect, query, analyze and 
present information 
2. Discovery Driven Data Mining:
1.Verification Driven Data Mining:
Querying and reporting, presenting the output in graphical, tabular and 
textual forms, through multi-dimensional analysis and through 
statistical analysis. 
There are four different Discovery Driven Data Mining approaches for at 
present:
Predictive Modeling including neural nets,
Link-analysis Technique
which attempts to establish links between
records,
Database Segmentation which partitions the data into collections of
related records, and
Deviation Detection which identifies point that do not fit in a segment.
FOUR FEATURES ARE:
Subject-oriented relevant data is stored as a single set in useful 
format 
Integrated data being stored in globally acceptable fashion
Non-volatile means the data warehouse is read-only
Time-variant data represents long term
The process of data mining is not a simple one as,
Data stored in Database  
It needs a variety of feedback loops to produce results of high 
expected quality,
At the core of the design of the data warehouse lies a multidimensional view of 
the data model. Some of them are as follows:
PROFESSION
ENGINEER
SECRETARY
TEACHER
Dimension Modeling
Lattice of Cuboids
Data Cube
COMMON SERVICES LAYER
Discovery and analysis of useful information from the World Wide Web. 
Web content mining is the process of extracting knowledge from the 
content of documents or their descriptions 
Web usage mining, also known as Web Log Mining, is the process of
extracting interesting patterns in web access logs.
Data mining predict future trends and behaviors, allowing businesses to make 
proactive, knowledge driven decisions.
Using a method called “neural segmentation”, a number of different
types of purchase patterns can be identified and then customer groupings can
be associated with this data.
To minimize the resources, it is necessary to identify what factors
affect the crop yield, out of such items as chemical fertilizers and additives.
DATA MINING
Sales and marketing analysis across all industries. 
Category management, vendor analysis, and marketing program 
effectiveness analysis in retail. 
Profitable lane or driver risk analysis in transportation. 
Profitability analysis or risk assessment in banking. 
Understand business trends and make better forecasting decisions. 
Category management, vendor analysis, and marketing program 
effectiveness analysis in retail. 
Claims analysis or fraud detection in insurance.
DATA WAREHOUSING
A good data warehousing & mining will... provide the
RIGHT data... to the RIGHT people... at the RIGHT time: RIGHT
NOW! While data warehouse organizes data for business
analysis, Internet has emerged as the standard for information
sharing. So the future of data warehousing & mining lies in their
accessibility from the Internet.
Convergence of the ERD, Dimensional and ETL Models makes 
completing the data modeling tasks Easier.
UML models also need to be available in conceptual schema 
for certain applications.
The future world would result in both convergence and 
standardization of data models for Data Warehousing.
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