1cisc 4631: data mining fall 2010 introduction to data mining (these slides are based on a variety...

1CISC 4631: Data Mining Fall 2010

Introduction to Data Mining

(these slides are based on a variety of sources)

2CISC 4631: Data Mining

Let’s Start By Seeing What you Know

Quick Quiz Do you know what Data Mining is? Do you know of any examples of Data

Mining?


What is Data Mining?

Data Mining has many definitions Non-trivial extraction of implicit,

previously unknown and potentially useful information from data

Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns

4CSRU4631: Data Mining

Alternative Names

Data Mining also known as or related to: Knowledge discovery in databases

(KDD) Knowledge extraction Data/pattern analysis Data archeology, data dredging,

information harvesting, business intelligence, etc.


Some Examples

Netflix and Amazon use data mining to recommend products (recommender systems)

Companies use data mining for marketing Who should be mailed a catalog Who should see what online ads (Google

Adwords) My WISDM project uses data mining

to determine (from your cell phone accelerometer data) who you are and what you are doing


Why Data Mining and Why Now?

Data Mining was not very popular until the last 10 years or so.

Quick Quiz: Why is it data mining popular now? What changed?


Why Mine Data?

There are now tremendous amounts of data that are automatically collected and warehoused. What are some examples? Web data, e-commerce Store purchases Bank/Credit Card transactions Cell phone GPS information


Why Mine Data?

What technological changes have helped make data mining so prevalent now? Computers: cheaper and more powerful

Smaller mobile devices are exploding in popularity

Disk and other storage: greater capacity and cheaper

RFID (radio frequency IDs), bar codes, etc

Increased use of on-line resources and Internet


Why Mine Data?

In business, competitive pressure is strong Provide better, customized services for

an edge (e.g. in Customer Relationship Management)

CRM is a relatively big deal now How do we get the most out of the customer

over the long run Example: Customer Churn Analysis


Scientific Viewpoint

Data collected at enormous speeds remote sensors on satellite telescopes scanning the skies microarrays generating gene

expression data scientific simulations

Traditional techniques infeasible

Data mining may help scientists in classifying and segmenting

data in hypothesis formation


Mining Large Data Sets - Motivation

Often information “hidden” in data that is not evident

Human analysts may take weeks to discover useful info

Much of the data is never analyzed at all

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

1995 1996 1997 1998 1999

The Data Gap

Total new disk (TB) since 1995

# of analysts

From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”


Mining Large Data Sets - Motivation

AT&T’s 26TB call detail database (2003)

Ebay 6PB, IRS 150TB data warehouse Yahoo has a 2PB DB to analyze

behavior of ½ billion web visitors/month (24 billion events/day)

Wal-Mart has a 583 TB database (2006)

Indexed web contains about 20 Billion pages

Sites like Facebook, Flicker & Twitter contain lots of data


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Data Deluge

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Amount of Data Created in One Year

Humans created/copied 161/281 Exabytes in 06/07 (IDC) 1 Exabyte = 1018

12 stacks of books stretching from Earth to Sun 3 million times the books ever written In 2010 will be 988 Exabytes Not all data stored at once

Much only temporarily UC Berkeley 2003 estimate:

5 Exabytes of new data created in 2002

US produces ~40% of new stored data worldwide


Data Growth Rate Twice as much information was created in

2002 as in 1999 (~30% growth rate) Other growth rate estimates even higher Very little data will ever be looked at by a

human Knowledge Discovery is NEEDED to

make sense and use of data Moore’s Law:

The information density on silicon-integrated circuits doubles every 18 to 24 months.

Parkinson’s Law: Work expands to fill the time available for its

completion


Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems

Traditional techniquesmay be unsuitable due to Enormity of data High dimensionality

of data Heterogeneous,

distributed nature of data

Origins of Data Mining

Artificial Intelligence / Machine Learning/

Pattern Recognition

Statistics

Data Mining

Database systems


Origins of Data Mining: My view

Biggest contributor is Machine Learning, which is a subfield of Artificial Intelligence Data Mining is a subset of machine learning

and focuses on practical problems of learning from data

Unlike machine learning, ultimate goal is not to build something that can learn as flexibly as a human

Does include other data analysis methods, like statistics

Databases do not play a central role in data mining. Most DM does not occur on data in a

conventional database, but rather extracts it to a flat file.

Data Mining methods do not work while data in a conventional (relational) database.


Statistics & Machine Learning vs. Data Mining

When compared to Data Mining: Statistics is:

more theory-based/based on mathematics as opposed to heuristic methods

more focused on testing hypothesesmakes more assumptions about the

data Machine learning is:

focused on improving performance of a learning agent in an environment


The KDD (Data Mining) Process

Data Mining is a process, sometimes referred to as a knowledge discovery process. In this process there is a data mining step that applies data mining algorithms to extract knowledge. About 80% of our class in on the data mining step.


Back to “What is a Data Mining”?

My opinion: Before determining whether

something is data mining need to consider:Is it a DM task? Is it implemented using a DM

method?Ideally, both parts will use data

mining but may be considered DM even if only is used for one.

We now will list the key DM tasks The course is organized around

these tasks

21

DATA MINING TASKSSecond Part of Introduction:

CISC 4631: Data Mining


2 Top Level Data Mining Tasks

At highest level, data mining tasks can be divided into: Prediction Tasks

Use some variables to predict unknown or future values of other variables

Description TasksFind human-interpretable patterns

that describe the data


Key Data Mining Tasks

Overview of major data mining tasks: Predictive

ClassificationRegressionDeviation/Anomaly Detection

DescriptiveClusteringAssociation Rule DiscoverySequential Pattern Discovery


Classification: Definition

Given a collection of records (training set ) Each record contains a set of attributes, one

of the attributes is the class, which is to be predicted.

Find a model for class attribute as a function of the values of other attributes. Model maps record to a class value

Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy

of the model. Can you think of examples of

classification tasks? We will see several shortly.


Classification Example

Tid Refund MaritalStatus

TaxableIncome Cheat

1 Yes Single 125K No

2 No Married 100K No

3 No Single 70K No

4 Yes Married 120K No

5 No Divorced 95K Yes

6 No Married 60K No

7 Yes Divorced 220K No

8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10

categoric

al

categoric

al

continuous

class

Refund MaritalStatus

TaxableIncome Cheat

No Single 75K ?

Yes Married 50K ?

No Married 150K ?

Yes Divorced 90K ?

No Single 40K ?

No Married 80K ?10

TestSet

Training Set

ModelLearn

Classifier

Task: Predict if someone cheats on their taxes


Classification: Application 1

Direct Marketing Goal: Reduce cost of mailing by targeting a

set of consumers likely to buy a new cell-phone product.

Approach: Use the data for a similar product introduced

before. We know which customers decided to buy and

which decided otherwise. This {buy, don’t buy} decision forms the class attribute

Collect various demographic, lifestyle, and company-interaction related information about all such customers.

Type of business, where they stay, how much they earn, etc.

Use this info as input attributes to learn a classifier model


Classification: Application 2 Fraud Detection

Goal: Predict fraudulent cases in credit card transactions

Approach: Use credit card transactions and info on

account-holders as attributes When and what does customer buy, how often pays

on time, etc Label past transactions as fraud or fair

transactions. This forms the class attribute. Learn a model for the class of the

transactions. Use this model to detect fraud by observing

credit card transactions on an account.



Customer Attrition/Churn: Goal: To predict whether a customer is

likely to be lost to a competitor. Approach:

Use detailed record of transactions with each of the past and present customers, to find attributes.

How often the customer calls, where he calls, what time-of-the day he calls most, his financial status, marital status, etc.

Label the customers as loyal or disloyal. Find a model for loyalty.



Sky Survey Cataloging Goal: To predict class (star or galaxy) of

sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory).

3000 images with 23,040 x 23,040 pixels per image.

Approach: Segment the image. Measure image attributes (features) - 40 of

them per object. Model the class based on these features. Success Story: Could find 16 new high red-

shift quasars, some of the farthest objects that are difficult to find!

From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996


Classifying Galaxies

Early

Intermediate

Late

Data Size: • 72 million stars, 20 million galaxies• Object Catalog: 9 GB• Image Database: 150 GB

Class: • Stages of

Formation

Attributes:• Image features, • Characteristics of

light waves received, etc.

Courtesy: http://aps.umn.edu


Regression

Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.

Greatly studied in statistics, neural network fields.

Examples: Predicting sales amounts of new product

based on advertising expenditure. Predicting wind velocities as a function of

temperature, humidity, air pressure, etc. Time series prediction of stock market

indices.


Clustering

Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that Data points in one cluster are similar to

one another Data points in different clusters are not

(less) similar Similarity Measures:

Euclidean distance if attributes are continuous

Problem-specific measures Can you think of any applications of

clustering?


Illustrating Clustering

Euclidean Distance Based Clustering in 3-D space.

Intracluster distancesare minimized

Intracluster distancesare minimized

Intercluster distancesare maximized

Intercluster distancesare maximized


Clustering: Application 1

Market Segmentation: Goal: subdivide a market into distinct

subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix.

Approach: Collect different attributes of customers

based on their geographical and lifestyle related information.

Find clusters of similar customers. Measure the clustering quality by

observing buying patterns of customers in same cluster vs. those from different clusters.


Clustering: Application 2 Document Clustering:

Goal: To find groups of documents that are similar to each other based on the important terms appearing in them.

Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster.

Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents.


Association Rule Discovery

Given a set of records each of which contain some number of items from a given collection Produce dependency rules which will

predict occurrence of an item based on occurrences of other items.

TID Items

1 Bread, Coke, Milk

2 Beer, Bread

3 Beer, Coke, Diaper, Milk

4 Beer, Bread, Diaper, Milk

5 Coke, Diaper, Milk

Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer}

Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer}


Association Rule Discovery: Application 1

Marketing and Sales Promotion: Let the rule discovered be {Bagels, … } --> {Potato Chips} Potato Chips as consequent => Can be used

to determine what should be done to boost its sales.

Bagels in the antecedent => Can be used to see which products would be affected if the store discontinues selling bagels.

Bagels in antecedent and Potato chips in consequent => Can be used to see what products should be sold with Bagels to promote sale of Potato chips!

Can help determine where to position store items


Association Rule Discovery: Application 2

Supermarket shelf management Goal: Identify items that are bought

together by many customers Approach: Process the point-of-sale data

collected with barcode scanners to find item dependencies

A “classic” rule -- If a customer buys diaper and milk, then he

is very likely to buy beer.


Sequential Pattern Discovery: Definition

Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events.

Rules are formed by first disovering patterns. Event occurrences in the patterns are governed by timing constraints.

(A B) (C) (D E)


Sequential Pattern Discovery: Examples

In telecommunications alarm logs, (Inverter_Problem Excessive_Line_Current) (Rectifier_Alarm) --> (Fire_Alarm)

In point-of-sale transaction sequences, Computer Bookstore:

(Intro_To_Visual_C) (C++_Primer) -->

(Perl_for_dummies,Tcl_Tk) Athletic Apparel Store:

(Shoes) (Racket, Racketball) --> (Sports_Jacket)


Deviation/Anomaly Detection Detect significant deviations from

normal behavior Applications:

Credit Card Fraud Detection

Network Intrusion Detection

Typical network traffic at University level may reach over 100 million connections per day


Challenges of Data Mining

Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data


What is (and is not) Data Mining?

Based on the definitions of data mining, are these DM or not? Finding a phone number in a directory

Not data mining (trivial) Grouping related documents returned by

search engine Is data mining

Identifying who has a disease based on symptoms

Is data mining (not trivial) Web search on keyword using search engine

May be data mining**

** More of an information retrieval task than data mining task, but since a search engine like Google does more than just keyword matching– it decides which web pages are important or not (a classification task that is part of DM) in order to get good results, the answer is not clear.


If you are Interested in Data Mining

Visit kdnuggets, an online newsletter and more http://www.kdnuggets.com You can arrange to have newsletter emailed to

you Also includes job openings

ACM SIGKDD is the professional organization associated with data mining ACM Special Interest Group (SIG) on data

mining Can join SIGKDD for $22 or for $54 can also

join ACM as student member Conferences

KDD, ICDM, DMIN, …

http://www.kdnuggets.com/


Course Projects

Projects must involve data mining May be research related

Examine some aspect of data mining May be application oriented

Solve a realistic, complex, problem May be a combination of both

Most problems involve some interesting aspect In some cases can be a survey/analysis paper

(i.e., just a report), but this will be atypical Can be done individually or in teams of 2

Ideally some projects can be published in a workshop or conference


Course Projects Output

A written report, similar to a workshop or conference paper

Two example workshop papers from last time course offered :

http://storm.cis.fordham.edu/~gweiss/papers/ubdm05-mccarthy.pdf

http://storm.cis.fordham.edu/~gweiss/papers/ubdm05-ciraco.pdf

For more examples: http://storm.cis.fordham.edu/~gweiss/publications.

html and look at the various workshop/conference papers

A presentation in class near end of semester Stretch goal: submit paper to a workshop

or conference I can help you





http://storm.cis.fordham.edu/~gweiss/publications.html

http://storm.cis.fordham.edu/~gweiss/publications.html


Course Projects

The sooner you start the better Think about:

What you know about What data you have access to What type of problems you are interested in Who you want to work with

I will provide some specific project ideas Areas include:

Classification, clustering, association rules Web and link mining, text mining, social

network analysis

1cisc 4631: data mining fall 2010 introduction to data mining (these slides are based on a variety...

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