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Data Mining
Adrian Tuhtan 004757481 CS157A Section1
Overview
Introduction Explanation of Data Mining Techniques Advantages Applications Privacy
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
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.
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.
Discovery of Knowledge
Data Mining Techniques
Classification Clustering Regression Association Rules
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.
Technique for Classification
Decision-Tree Classifiers
Job
Income
Job
Income Income
CarpenterEngineer Doctor
Bad Good Bad Good Bad Good
<30K <40K <50K>50K >90K>100K
Predicting credit risk of a person with the jobs specified.
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’
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
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
Regression Example graph:
Line of Best Fit Curve Fitting
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.
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.
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.
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
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
Applications of Data Mining
(4)
Source IDC 1998
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
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/regression.html http://wwwmaths.anu.edu.au/~steve/pdcn.pdf