Data Mining

1
Data Mining

• Adrian Tuhtan
• 004757481
• CS157A
• Section1

2
Overview

• Introduction
• Explanation of Data Mining Techniques
• Advantages
• Applications
• Privacy

3
Data Mining

• What is Data Mining?
• The process of semi automatically analyzing
  large databases to find useful patterns
  (Silberschatz)
• KDD Knowledge Discovery in Databases (3)
• Attempts to discover rules and patterns from
  data
• Discover Rules ? Make Predictions
• Areas of Use
• Internet Discover needs of customers
• Economics Predict stock prices
• Science Predict environmental change
• Medicine Match patients with similar problems ?
  cure

4
Example of Data Mining

• Credit Card Company wants to discover information
  about clients from databases. Want to find
• Clients who respond to promotions in Junk Mail
• Clients that are likely to change to another
  competitor
• Clients that are likely to not pay
• Services that clients use to try to promote
  services affiliated with the Credit Card Company
• Anything else that may help the Company provide/
  promote services to help their clients and
  ultimately make more money.

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Data Mining Data Warehousing

• Data Warehouse is a repository (or archive) of
  information gathered from multiple sources,
  stored under a unified schema, at a single site.
  (Silberschatz)
• Collect data ? Store in single repository
• Allows for easier query development as a single
  repository can be queried.
• Data Mining
• Analyzing databases or Data Warehouses to
  discover patterns about the data to gain
  knowledge.
• Knowledge is power.

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Discovery of Knowledge
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Data Mining Techniques

• Classification
• Clustering
• Regression
• Association Rules

8
Classification

• Classification Given a set of items that have
  several classes, and given the past instances
  (training instances) with their associated class,
  Classification is the process of predicting the
  class of a new item.
• Therefore to classify the new item and identify
  to which class it belongs
• Example A bank wants to classify its Home Loan
  Customers into groups according to their response
  to bank advertisements. The bank might use the
  classifications Responds Rarely, Responds
  Sometimes, Responds Frequently.
• The bank will then attempt to find rules about
  the customers that respond Frequently and
  Sometimes.
• The rules could be used to predict needs of
  potential customers.

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Technique for Classification

• Decision-Tree Classifiers

Job
Doctor
Engineer
Carpenter
gt100K
lt30K
lt40K
lt50K
gt50K
gt90K
Predicting credit risk of a person with the jobs
specified.
10
Clustering

• Clustering algorithms find groups of items that
  are similar. It divides a data set so that
  records with similar content are in the same
  group, and groups are as different as possible
  from each other. (2)
• Example Insurance company could use clustering
  to group clients by their age, location and types
  of insurance purchased.
• The categories are unspecified and this is
  referred to as unsupervised learning

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Clustering

• Group Data into Clusters
• Similar data is grouped in the same cluster
• Dissimilar data is grouped in the same cluster
• How is this achieved ?
• K-Nearest Neighbor
• A classification method that classifies a point
  by calculating the distances between the point
  and points in the training data set. Then it
  assigns the point to the class that is most
  common among its k-nearest neighbors (where k is
  an integer).(2)
• Hierarchical
• Group data into t-trees

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Regression

• Regression deals with the prediction of a value,
  rather than a class. (1, P747)
• Example Find out if there is a relationship
  between smoking patients and cancer related
  illness.
• Given values X1, X2... Xn
• Objective predict variable Y
• One way is to predict coefficients a0, a1, a2
• Y a0 a1X1 a2X2 anXn
• Linear Regression

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Regression

• Example graph
• Line of Best Fit
• Curve Fitting

14
Association Rules

• An association algorithm creates rules that
  describe how often events have occurred
  together. (2)
• Example When a customer buys a hammer, then 90
  of the time they will buy nails.

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Association Rules

• Support is a measure of what fraction of the
  population satisfies both the antecedent and the
  consequent of the rule(1, p748)
• Example
• People who buy hotdog buns also buy hotdog
  sausages in 99 of cases. High Support
• People who buy hotdog buns buy hangers in 0.005
  of cases. Low support
• Situations where there is high support for the
  antecedent are worth careful attention
• E.g. Hotdog sausages should be placed in near
  hotdog buns in supermarkets if there is also high
  confidence.

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Association Rules

• Confidence is a measure of how often the
  consequent is true when the antecedent is true.
  (1, p748)
• Example
• 90 of Hotdog bun purchases are accompanied by
  hotdog sausages.
• High confidence is meaningful as we can derive
  rules.
• Hotdog bun? Hotdog sausage
• 2 rules may have different confidence levels and
  have the same support.
• E.g. Hotdog sausage ? Hotdog bun may have a much
  lower confidence than Hotdog bun ? Hotdog sausage
  yet they both can have the same support.

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Advantages of Data Mining

• Provides new knowledge from existing data
• Public databases
• Government sources
• Company Databases
• Old data can be used to develop new knowledge
• New knowledge can be used to improve services or
  products
• Improvements lead to
• Bigger profits
• More efficient service

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Uses of Data Mining

• Sales/ Marketing
• Diversify target market
• Identify clients needs to increase response rates
• Risk Assessment
• Identify Customers that pose high credit risk
• Fraud Detection
• Identify people misusing the system. E.g. People
  who have two Social Security Numbers
• Customer Care
• Identify customers likely to change providers
• Identify customer needs

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Applications of Data Mining

• (4)

Source IDC 1998
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Privacy Concerns

• Effective Data Mining requires large sources of
  data
• To achieve a wide spectrum of data, link multiple
  data sources
• Linking sources leads can be problematic for
  privacy as follows If the following histories of
  a customer were linked
• Shopping History
• Credit History
• Bank History
• Employment History
• The users life story can be painted from the
  collected data

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References

• Silberschatz, Korth, Sudarshan, Database System
  Concepts, 5th Edition, Mc Graw Hill, 2005
• http//www.twocrows.com/glossary.htm, Two Crows,
  Data Mining Glossary
• http//en.wikipedia.org/wiki/Data_mining,
  Wikipedia
• http//phoenix.phys.clemson.edu/tutorials/excel/re
  gression.html
• http//wwwmaths.anu.edu.au/steve/pdcn.pdf