Download Data Mining and Data Warehousing Introduction to Data Mining

Data Mining and Data Warehousing
Introduction to Data Mining
Motivation
To get complete understanding about how important information is extracted
from raw data and how that information helps people in making day to day
choices.
Introduction ppt (For your convenience you can get them inside Learn More
Quadrant)
Learning Objectives
By the end of this module, the learner will be able to:
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Define data mining
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Compare various data mining tasks
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Identify various applications of data mining
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Discover the various aspects of data mining
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Justify the challenges in data mining
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Imagine various scenarios where data mining can be applied
Prerequisite
For this topic, the learner should have an understanding of basic database
concepts such as schema, ER model and Structured Query language. It gives a
basic idea about how mining of data is used in various algorithms as well as how it
is related to our day to day activities.
Suggested Time
150 mins
Concept
Data Mining
Process of semi-automatically analyzing large databases to find patterns that are:
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Valid: holds new data with some certainty
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Novel: non-obvious to the system
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Useful: should be possible to act on the item
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Understandable: humans should be able to interpret the pattern
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Also known as Knowledge Discovery in Databases (KDD)
Need for data mining
Here are the reasons listed below:
In the field of Information technology we have huge amount of data available that
needs to be turned into useful information. This information further can be used
for various applications such as market analysis, fraud detection, customer
retention, production control, science exploration etc.
Applications
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Banking (loan/credit card approval): predict the good customers based on old
customers
Customer relationship management: Identify those who are likely to leave for
a competitor.
Targeted marketing: Identify likely responders to promotions
Fraud detection: Telecommunications, financial transactions-from an online
stream of event, identify fraudulent events
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Manufacturing and production: Automatically adjust knobs when process
parameter changes
Medicine: Disease outcome, effectiveness of treatments. Analyze patient
disease history. Find relationship between diseases
Molecular/Pharmaceutical: Identify new drugs
Scientific data analysis: Identify new galaxies by searching for sub clusters
Web site/store design and promotion: Find affinity of visitor to pages and
modify layout
Major Data Mining Tasks
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Classification: Predicting an item class
Association Rule Discovery: descriptive
Clustering: descriptive, finding groups of items
Sequential Pattern Discovery: descriptive
Deviation Detection: predictive, finding changes
Forecasting: predicting a parameter value
Description: describing a group
Link analysis: finding relationships and associations
Bio Data Mining: DNA analysis
Data Mining in use currently
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The Government uses Data Mining to track fraud
A Supermarket becomes an information broker
Basketball teams use it to track game strategy
Cross Selling
Target Marketing
Holding on to Good Customers
Weeding out Bad Customers
Major concerns in Data Mining
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Mining various and new kinds of knowledge
To make data mining user interactive, that includes, incorporation of
background knowledge.
Make data mining results visual and directly usable by humans.
Make mining algorithm efficient and scalable.
Use new technologies like cloud computing and cluster computing to make
algorithms parallel and distributed.
Preserve privacy while mining social data.
Efficiently mining shared and networked repositories.
Challenges in Data Mining
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Intelligent data analysis in Bio-Informatics
Mining with data streams (in continuous, real-time, dynamic data
environments)
Mining complex knowledge from complex data
Data Mining for Biological and Environmental problems
Security, privacy and data integrity
Illustration
The process of mining Data using the Data Mining techniques will be as shown in
the figure.