1CS 660: Data Mining For Decision Making

Lecture 1 (Week 1)

Varun Dutt

School of Computing and Electrical Engineering

School of Humanities and Social Sciences

Indian Institute of Technology Mandi, India

Scaling the Heights

Course Instructor

Prof. Varun Dutt

School of Computing and Electrical Engineering

School of Humanities and Social Sciences

Indian Institute of Technology, Mandi

PWD Rest House 2nd Floor, Mandi - 175 001, H.P., India

Phone: +91-1905-267041

Email: varun@iitmandi.ac.in

Office Hours: Only with a prior appointment

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A Little About Me! In the office

Qualifications

M.S. degrees in Software Engineering, Engineering and Public Policy, and Rational Simulation (cognitive modeling) from Carnegie Mellon University

Ph.D. in Engineering and Public Policy from Carnegie Mellon University

Post-doctoral fellowship from Carnegie Mellon University

Since 2012 at Indian Institute of Technology, Mandi, India

Research interests

Artificial intelligence and cognitive modeling, Human-Computer Interaction,Environmental decision making, Judgment and Decision Making

Professional Experience

Served as a Software Engineer in Tata Consultancy Services (TCS) and in MothersonSumi INfotech and Designs Ltd.

Serves as Knowledge Editor of a financial daily, Financial Chronicle

Serves as Lead Author on Chapter 2 on UN IPCCs AR5 (WG III) report

4A Little About Me! At home

ABBA Fan

Married to Dr. Rajeshwari Dutt with a cute little daughter

Get no sleep!

Do a lot of writing and have a back problem

I have a TA to help!

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Teaching Assistants

- Sanjay Rathee, Ph.D. student, SCEE, IIT Mandi. Email:

sanjay_rathee@students.iitmandi.ac.in (Has been working on

parallelizing A-priori algorithm recently.)

- Akash Porwal, Ph.D. student, SCEE, IIT Mandi. Email:

porwalakash.1989@gmail.com (Has recently joined and is working on

electrical problems concerning Solar Photovoltaics)

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What about you folks?

Please introduce yourselves

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Announcements

Syllabus

Your Grade:

30% Final exam

20% Surprise Quizzes

10% Class Participation

20% Class Assignments

20% Class Project

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8Course Logistics

- Please dont copy or plagiarize! - Being an AI researcher, I know how to catch it

- If found, consequences will be catastrophic!

- If you did copy, then please cite the sources as

(author, date). E.g., (Dutt, 2012)

An Example (Witten, Frank, & Hall, 2011)

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Data mining is defined as the process of discovering structural patterns in data.

The process must be automatic or (more usually) semiautomatic.

The patterns discovered must be meaningful in that they lead to some

advantage, usually an economic one.

The data is invariably present in substantial quantities.

Data Mining: What is it? (Witten, Frank, & Hall, 2011)

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Example

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If tear production rate = reduced then

recommendation = none

Otherwise, if age = young and astigmatic =

no then recommendation = soft

Structural Description (Pattern) in Data

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Weather Dataset

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In this case there are four attributes: outlook, temperature,

humidity, and windy. The outcome is whether to play or not.

A set of rules learned from this information might look like this:

If outlook = sunny and humidity = high then play = no

If outlook = rainy and windy = true then play = no

If outlook = overcast then play = yes

If humidity = normal then play = yes

If none of the above then play = yes

Structural Description (Pattern) in Data (also, called a Decision List)

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These rules are meant to be interpreted in order:

The first one; then, if it doesnt apply, the second; and so on. A set of rules that are intended to be

interpreted in sequence is called a decision list.

Interpreted as a decision list, the rules correctly classify all of the examples in the table, whereas

taken individually, out of context, some of the rules

are incorrect. For example, the rule if humidity =

normal then play = yes gets one of the examples

wrong (check which one).

Decision List

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Weather Dataset: Two of the attributestemperature and humidityhave numeric values

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Structural Description (Classification Rules)

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For this example, there must be inequalities involving these attributes

rather than simple equality tests as in the former case.

This is called a numeric-attribute problemin this case, a mixed-attribute problem because not all attributes are numeric.

Now the first rule given earlier might take the formIf outlook = sunny and humidity > 83 then play = no

Association Rules

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Association Rules

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Data Cleaning (scrubbing, also called data cleansing), is the process of amending or removing data in a database that is

incorrect, incomplete, improperly formatted, or duplicated. It is a time

consuming activity often done in a semi-automated manner.

Missing Values: Missing values are frequently indicated by out-of-range entries. Example: A negative number (e.g., 1) in a numeric field that is normally only positive, or a 0 in a numeric field that can

never normally be 0. For nominal attributes, missing values may be

indicated by blanks or dashes.

Inaccurate Values: Pepsi somewhere and Pepsi-Cola somewhere else. Typographical errors. Example: Super-market seller uses her

own cards for discounts to those who forgot their cards.

Preparing Input Data for Data Mining

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Web-mining: Prestige of a web-page based upon how many link to it (PageRank)

Decisions involving judgments (Banks use data-mining while giving you loans accept or reject cases)

Screening images (oil slicks or not in sea using satellite data)

Load forecasting in Electricity Industry

Diagnosing faults in machines in Industry

Marketing and Sales (Pharmaceutical Industry Patient Journeys, Market-Basket Analysis (Pepsi and Diapers on

Thursdays), Discount or Loyalty Cards to Collect Data

Applications of Data Mining in Real World

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Activities

Read Witten, Frank, and Hall, 2011: Chapter 1 (up to page 15 before CPU performance; 21-29, 51-52,

58-60):

http://www.cse.hcmut.edu.vn/~chauvtn/data_mining/

Texts/[7]%20Data%20Mining%20-

%20Practical%20Machine%20Learning%20Tools%2

0and%20Techniques%20(3rd%20Ed).pdf

Read Singhal, 2011

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Thank you!

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Comments and Questions most welcome!