April 12, 2023 1

Data Mining: Concepts and Techniques

©Jiawei Han and Micheline Kamber

Intelligent Database Systems Research Lab

School of Computing Science

Simon Fraser University, Canada

http://www.cs.sfu.ca

Some of these slides are taken with some modifications from:

April 12, 2023 2

Acknowledgements

This work on this set of slides started with Han’s tutorial

for UCLA Extension course in February 1998

Dr. Hongjun Lu from Hong Kong Univ. of Science and

Technology taught jointly with me a Data Mining Summer

Course in Shanghai, China in July 1998. He has

contributed many excellent slides to it

Some graduate students have contributed many new

slides in the following years. Notable contributors include

Eugene Belchev, Jian Pei, and Osmar R. Zaiane (now

teaching in Univ. of Alberta).

April 12, 2023 3

Where to Find More Slides?

Tutorial sections (MS PowerPoint files):

http://www.cs.sfu.ca/~han/dmbook

Other conference presentation slides (.ppt):

http://db.cs.sfu.ca/ or http://www.cs.sfu.ca/~han

Research papers, DBMiner system, and other related

information:

http://db.cs.sfu.ca/ or http://www.cs.sfu.ca/~han

April 12, 2023 4

Introduction

Motivation: Why data mining?

What is data mining?

Data Mining: On what kind of data?

Data mining functionality

Are all the patterns interesting?

Classification of data mining systems

Major issues in data mining

April 12, 2023 5

Motivation: “Necessity is the Mother of Invention”

Data explosion problem

Automated data collection tools and mature database technology

lead to tremendous amounts of data stored in databases, data

warehouses and other information repositories

We are drowning in data, but starving for knowledge!

Solution: Data warehousing and data mining

Data warehousing and on-line analytical processing

Extraction of interesting knowledge (rules, regularities, patterns,

constraints) from data in large databases

April 12, 2023 6

Evolution of Database Technology

1960s: Data collection, database creation, IMS and network DBMS

1970s: Relational data model, relational DBMS implementation

1980s: RDBMS, advanced data models (extended-relational, OO,

deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)

1990s—2000s: Data mining and data warehousing, multimedia databases, and

Web databases

April 12, 2023 7

What Is Data Mining?

Data mining (knowledge discovery in databases):

Extraction of interesting (non-trivial, implicit, previously

unknown and potentially useful) information or patterns from data in large databases

Alternative names and their “inside stories”: Data mining: a misnomer? Knowledge discovery(mining) in databases (KDD), knowledge

extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

Not data mining if it handles only small amounts of data retrieves data in answer to queries

April 12, 2023 8

Why Data Mining? — Potential Applications

Database analysis and decision support Market analysis and management

customer relation management, market basket analysis

Risk analysis and management

Forecasting, quality control, competitive analysis

Fraud detection and management

Other Applications Text mining (news group, email, documents) and Web analysis.

Intelligent query answering

April 12, 2023 9

Market Analysis and Management

Where are the data sources for analysis? Credit card transactions, clickstreams, customer forms, shopping baskets

Target marketing Clusters of “model” customers with same characteristics: interest, income level

Determine customer purchasing patterns over time Cross-market analysis

Identify associations between product sales, use to predict purchases

Customer profiling what types of customers buy what products? (clustering or classification)

Identifying customer requirements identify best products for different customers, predict factors to attract new

customers

April 12, 2023 10

Other Applications

Sports IBM Advanced Scout analyzed NBA game statistics (shots

blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat

Astronomy JPL and the Palomar Observatory discovered 22 quasars with

the help of data mining

Medical Research Large insurance companies use data mining to study questions

such as the effectiveness of various kinds of antibiotic in reducing recurrent infections

April 12, 2023 11

Data Mining: A KDD Process

Data mining: the core of knowledge discovery process.

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

April 12, 2023 12

Steps of a KDD Process

Learning the application domain: relevant prior knowledge and goals of application

Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation:

Find useful features, dimensionality/variable reduction, invariant representation.

Choosing functions of data mining summarization, classification, regression, association, clustering.

Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation

visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge

April 12, 2023 13

Data Mining and Business Intelligence

Increasing potentialto supportbusiness decisions End User

Business Analyst

DataAnalyst

DBA

MakingDecisions

Data Presentation

Visualization Techniques

Data MiningInformation Discovery

Data Exploration

OLAP, MDA

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

Data SourcesPaper, Files, Information Providers, Database Systems, OLTP

April 12, 2023 14

Architecture of a Typical Data Mining System

Data Warehouse

Data cleaning & data integration Filtering

Databases

Database or data warehouse server

Data mining engine

Pattern evaluation

Graphical user interface

Knowledge-base

April 12, 2023 15

Data Mining: On What Kind of Data?

Relational databases Data warehouses Transactional databases Advanced DB and information repositories

Object-oriented and object-relational databases Spatial databases Time-series data and temporal data Text databases and multimedia databases Heterogeneous and legacy databases WWW

April 12, 2023 16

Data Mining Functionalities (1)

Concept description: Characterization and discrimination Generalize, summarize, and contrast data

characteristics, e.g., dry vs. wet regions

Association (correlation and causality) Multi-dimensional vs. single-dimensional association age(X, “20..29”) ^ income(X, “20..29K”) buys(X,

“PC”) [support = 2%, confidence = 60%] contains(T, “computer”) contains(x, “software”) [1%,

75%]

April 12, 2023 17

Data Mining Functionalities (2)

Classification and Prediction Finding models (functions) that describe and distinguish classes

or concepts for future prediction E.g., classify countries based on climate, or classify cars based

on gas mileage Presentation: decision-tree, classification rule, neural network Prediction: Predict some unknown or missing numerical values

Cluster analysis Class label is unknown: Group data to form new classes, e.g.,

cluster houses to find distribution patterns Clustering based on the principle: maximizing the intra-class

similarity and minimizing the interclass similarity

April 12, 2023 18

Data Mining Functionalities (3)

Outlier analysis Outlier: a data object that does not comply with the general behavior of

the data

It can be considered as noise or exception but is quite useful in fraud

detection, rare events analysis

Trend and evolution analysis Trend and deviation: regression analysis

Sequential pattern mining, periodicity analysis

Similarity-based analysis

Other pattern-directed or statistical analyses

April 12, 2023 19

Are All the “Discovered” Patterns Interesting?

A data mining system/query may generate thousands of patterns,

not all of them are interesting.

Suggested approach: Human-centered, query-based, focused mining

Interestingness measures: A pattern is interesting if it is easily

understood by humans, valid on new or test data with some degree

of certainty, potentially useful, novel, or validates some hypothesis

that a user seeks to confirm

Objective vs. subjective interestingness measures:

Objective: based on statistics and structures of patterns, e.g., support,

confidence, etc.

Subjective: based on user’s belief in the data, e.g., unexpectedness,

novelty, actionability, etc.

April 12, 2023 20

Can We Find All and Only Interesting Patterns?

Find all the interesting patterns: Completeness Can a data mining system find all the interesting patterns?

Association vs. classification vs. clustering

Search for only interesting patterns: Optimization Can a data mining system find only the interesting patterns?

Approaches

First generate all the patterns and then filter out the

uninteresting ones.

Generate only the interesting patterns—mining query

optimization

April 12, 2023 21

Data Mining: Confluence of Multiple Disciplines

Data Mining

Database Technology

Statistics

OtherDisciplines

InformationScience

MachineLearning Visualization

April 12, 2023 22

Data Mining: Classification Schemes

General functionality Descriptive data mining

Predictive data mining

Different views, different classifications Kinds of databases to be mined

Kinds of knowledge to be discovered

Kinds of techniques utilized

Kinds of applications adapted

April 12, 2023 23

A Multi-Dimensional View of Data Mining Classification

Databases to be mined Relational, transactional, object-oriented, object-relational,

active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc.

Knowledge to be mined Characterization, discrimination, association, classification,

clustering, trend, deviation and outlier analysis, etc. Multiple/integrated functions and mining at multiple levels

Techniques utilized Database-oriented, data warehouse (OLAP), machine learning,

statistics, visualization, neural network, etc. Applications adapted

Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc.

April 12, 2023 24

Major Issues in Data Mining (1)

Mining methodology and user interaction Mining different kinds of knowledge in databases Interactive mining of knowledge at multiple levels of abstraction Incorporation of background knowledge Data mining query languages and ad-hoc data mining Expression and visualization of data mining results Handling noise and incomplete data Pattern evaluation: the interestingness problem

Performance and scalability Efficiency and scalability of data mining algorithms Parallel, distributed and incremental mining methods

April 12, 2023 25

Major Issues in Data Mining (2)

Issues relating to the diversity of data types Handling relational and complex types of data Mining information from heterogeneous databases and global

information systems (WWW) Issues related to applications and social impacts

Application of discovered knowledge Domain-specific data mining tools Intelligent query answering Process control and decision making

Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem

Protection of data security, integrity, and privacy

April 12, 2023 26

Summary

Data mining: discovering interesting patterns from large amounts of data

A natural evolution of database technology, in great demand, with wide applications

A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation

Mining can be performed in a variety of information repositories Data mining functionalities: characterization, discrimination,

association, classification, clustering, outlier and trend analysis, etc. Classification of data mining systems Major issues in data mining

April 12, 2023 27

Where to Find References?

Data mining and KDD: Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery

Database field: Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE,

EDBT, DASFAA Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc.

AI and Machine Learning: Conference proceedings: Machine learning, AAAI, IJCAI, etc. Journals: Machine Learning, Artificial Intelligence, etc.

Statistics: Conference proceedings: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc.

Visualization: Conference proceedings: CHI, etc. Journals: IEEE Trans. visualization and computer graphics, etc.

April 12, 2023 28

References

U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in

Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.

J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan

Kaufmann, 2000.

T. Imielinski and H. Mannila. A database perspective on knowledge discovery.

Communications of ACM, 39:58-64, 1996.

G. Piatetsky-Shapiro, U. Fayyad, and P. Smith. From data mining to knowledge

discovery: An overview. In U.M. Fayyad, et al. (eds.), Advances in Knowledge

Discovery and Data Mining, 1-35. AAAI/MIT Press, 1996.

G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases.

AAAI/MIT Press, 1991.

April 12, 2023 2929

Data Mining for Web Sites

April 12, 2023 30

Data Mining for Web Sites

Clickstream Mining KDD Cup Mining site databases

April 12, 2023 31

Clickstream Mining

Kinds of data available Raw Data Aggregations and Cleanup

Kinds of questions you can ask Some of the cautions

April 12, 2023 32

Clickstream Mining What is a clickstream?

The record of every page request from every visitor to your site

What does it typically contain? Date/time of the page request IP address of visitor Page object being requested (whole page or a

frame, image, etc.) Type of request (get, submit) Referrer Browser making request

April 12, 2023 33

Data Cleaning Eliminate search engines and bots?

They follow atypical patterns through a site Can’t typically do by IP address. Many hits within a very short time period exactly one hit on each link with a depth first or breadth-first

pattern Hits at the same time every day, at unusual times.

Eliminate internal testers? Typically can do by IP address. Harder if both developers

and customers are internal and addressing is dynamic.

Eliminate certain sites? AOL reassigns IP at every request Previous experience suggests that you get a lot of valueless

hits from, e.g., the .edu domain.

April 12, 2023 34

Aggregations/Dimensions

Aggregate or process individual log requests to get richer dimensions Date and Time Visitors Page object Session Path

April 12, 2023 35

Some Aggregations Date and Time

Separate them! Reference to standard such as GMT If multiple servers, need very accurate

synchronization Visitors

anonymous, by IP only. Track within one session (probably)

Cookie. Track visitor within one session reliably, possibly across sessions

Registration. Have some significant data. Name, email address, etc.

April 12, 2023 36

Some More Aggregations

Page object. Group together objects on one “page”. Frames, images Add meta-information/page characteristics if available. DB-based,

portal-based, XML-based web-sites.

Session One “visit” by a user Typically, all connections from the same IP address without a gap

of at least a certain length. Login to logout or timeout of you require login.

Path Sequence of pages visited during one session by one visitor

April 12, 2023 37

What’s It Good For? Kinds of questions you can answer solely through

clickstream data On what pages did people spend a relatively long time? What was the last page typically viewed? Did people follow

“recommender” links? Where did referrals come from? Where did referrals who spent significant time come from?

Mostly questions about the web site itself Mostly descriptive statistics with relatively simple

analyses once the cleanup and aggregation is done Interpreting the answers to the questions requires an

understanding of the domain

April 12, 2023 38

Some cautions

Visitor Counts: DHCP, caching, AOL Session definition: false positives AND negatives Path through site: caching and “go” menu “Time on site”: count UP TO last request, but not

time on last page.

April 12, 2023 39

KDD Cup Annual challenge problem at the ACM KDD

conference. In 2000, it involved clickstream mining. In 2001,

Prediction of Molecular Bioactivity for Drug Design

Prediction of Gene/Protein Function and Localization

In 2002, Task 1: Information Extraction from Biomedical Articles

Task 2: Yeast Gene Regulation Prediction

April 12, 2023 40

KDD Cup, 2000

Five questions: Given a set of page views, will the visitor view another page

on the site or will the visitor leave? Given a set of page views, which product brand will the

visitor view in the remainder of the session? Given a set of purchases over a period of time, characterize

visitors who spend more than $12 (order amount) on an average order at the site.

Given a set of page views, characterize killer pages, i.e., pages after which users leave the site.

Given a set of page views, characterize which product brand a visitor will view in the remainder of the session?

http://www.ecn.purdue.edu/KDDCUP/ http://robotics.Stanford.EDU/~ronnyk/kddCupTalk.ppt

April 12, 2023 4141

Other Kinds of Data Mining For the Web

April 12, 2023 42

Site Databases Kinds of data available Data cleanup and aggregation a lot easier Kinds of questions you might ask Recommender systems

Collaborative filtering Simple correlations

Can get really fancy: Amazon

April 12, 2023 43

Kinds of Data Available User data elicited from user:

Ordering information Preferences, likes, dislikes Personal information such as name, address, credit card

Enriched User Data (e.g., Acxiom Infobase) age gender marital status vehicle lifestyle own/rent

Product Data

April 12, 2023 44

Kinds of Questions

What were typical items purchased? What were typical items purchased by high

spenders? For people who chose X, what else might they

like? Based on known characteristics Based on statistical patterns

April 12, 2023 45

Combining Data

Using just clickstream data can give you some information relevant to a website.

Additional questions available if you combine: Clickstream Site Databases Enriched data from other databases

April 12, 2023 46

Kinds of Questions

What are general characteristics of people who spend a lot of time on the site? (e.g., educational level)

Which pages are visited by people who actually buy? Which referring sites lead to purchases, and which to

“curiosity” visits?

April 12, 2023 47

Issues, Concerns

Merging data Just about requires login. So when do you require it? Cookies may be misleading. One user, multiple

systems; one system, multiple users Need to know domain, to interpret results

April 12, 2023 48

Recommender Systems Very common addition to e-commerce sites Editorial recommenders Content Filtering Recommenders Collaborative Filtering Recommenders Hybrids

April 12, 2023 49

Editorial Filtering Recommendations made by a person Not new, obviously Web has made them much more accessible

www.imdb.com. Movie reviews mysteryguide.com. Mystery book reviews Search, browse capabilities

Most prevalent for media: books, movies, CDs Advantages:

Detailed, "accurate" reviews. Add context

Disadvantages Coverage is limited No personalization Some areas (e.g., travel) heavily dominated by commercial sites

April 12, 2023 50

Content Filtering Find documents "like this one" Attributes for comparison can be

meta-data author subject director

These are typically simple statistics document content

bag-of-words, vectors keywords

All the categorization techniques we have discussed

April 12, 2023 51

Collaborative Filtering DB of user ratings/preferences. Explicit or inferred

from purchases For case to be predicted or recommended

Determine nearest neighbors based on known shared data Weight neighbors’ choices based on “nearness”. Return top predictions or recommendations

Can use other algorithms for choosing cases to predict from. (e.g., neural nets)

All assume some dimensions on which we have (probably incomplete) data for each case.

All are automatic, not involving human judgment Lyle Ungar has an excellent set of links:

http://www.cis.upenn.edu/~ungar/CF/

April 12, 2023 52

Amazon recommender systems Rich set of recommendations using multiple

techniques Content Filtering: Books like this, authors like this:

straight descriptive statistics. (Caution: control for overall frequency?)

Collaborative filtering: individual recommendations, based on purchases and ratings

Editorial Filtering: Lists provided by users. Hybrid: Best Seller lists, current rank of books.

Access recommender system directly: I own it Rate it Not interested exclude this item why was this recommended?

April 12, 2023 53

Tuning Amazon's Recommender System

Individual recommendations are based on purchases and ratings.

Access recommender system directly: I own it Rate it Not interested exclude this item add this item

Why was this recommended?

April 12, 2023 54

Web Privacy

April 12, 2023 55

Introduction Privacy is a significant issue

On the web Who knows what about you? What are they doing with it

For data mining Who has collected data on you (not just on the web) Why did they collect it? What else are they entitled to do with it?

Who owns data about you? How can society best control privacy abuses?

Voluntary compliance and market forces? Government regulation?

April 12, 2023 56

Homework Assignment1. How many clicks from the home page did it take you to reach

the privacy policy?

2. What information do they collect? For what is it used?

3. With whom do they share information?

4. If they change their policy how are you notified?

5. Can you ask that information maintained be limited? How?

6. Can you see what information is maintained about you? Ask that information be removed? Ask that it be corrected? How?

April 12, 2023 57

Who Knows What about You?

Clickstreams and Cookies Brief overview of some of what’s out there, just to

get you thinking :-) http://www.privacy.net/analyze/ http://www.junkbusters.com/ht/en/cookies.html

April 12, 2023 58

How Do We Control It? The US has tended toward technical, marketplace and voluntary

standards. Patchwork of state and local laws. Several laws proposed at the national level, but none has passed Serious Freedom of Speech concerns, both directions Much industrial pressure to keep voluntary

The European Union has passed the EU Data Privacy Directive. Articles 25 and 26 prohibit exchanging data with countries which do not

comply http://www.cdt.org/privacy/eudirective/EU_Directive_.html

U.S.has proposed a Safe Harbor register and be certified as complying with safe harbor provisions If certified, acceptable as alternative for EU data exchange http://www.exports.gov/safeharbor/

April 12, 2023 59

Safe Harbor Provisions

In place Gradually being adopted; 156 organizations listed,

compared to 30 a year ago. Continues to be debated; 156 is miniscule! Both enforcement of Safe Harbor compliance and EU

enforcement still issues http://www.europemedia.net/showfeature.asp?ArticleID=8608 http://www.house.gov/commerce/hearings/03082001-49/08082001.htm http://www.useu.be/ISSUES/over0817.html

April 12, 2023 60

Safe Harbor Provisions Notice Choice Onward Transfer (Transfers to Third

Parties) Access Security Data integrity Enforcement

April 12, 2023 61

Notice

Notice: Organizations must notify individuals about the purposes for which they collect and use information about them. They must provide information about how individuals can contact the organization with any inquiries or complaints, the types of third parties to which it discloses the information and the choices and means the organization offers for limiting its use and disclosure.

April 12, 2023 62

Choice

Choice: Organizations must give individuals the opportunity to choose (opt out) whether their personal information is to be disclosed to a third party or to be used for a purpose incompatible with the purpose for which it was originally collected or subsequently authorized by the individual. For sensitive information, affirmative or explicit (opt in) choice must be given if the information is to be disclosed to a third party or used for a purpose other than its original purpose or the purpose authorized subsequently by the individual.

April 12, 2023 63

Onward Transfer

Onward Transfer (Transfers to Third Parties): To disclose information to a third party, organizations must apply the notice and choice principles. Where an organization wishes to transfer information to a third party that is acting as an agent(1), it may do so if it makes sure that the third party subscribes to the safe harbor principles or is subject to the Directive or another adequacy finding. As an alternative, the organization can enter into a written agreement with such third party requiring that the third party provide at least the same level of privacy protection as is required by the relevant principles.

April 12, 2023 64

Access

Access: Individuals must have access to personal information about them that an organization holds and be able to correct, amend, or delete that information where it is inaccurate, except where the burden or expense of providing access would be disproportionate to the risks to the individual's privacy in the case in question, or where the rights of persons other than the individual would be violated.

April 12, 2023 65

Security

Security: Organizations must take reasonable precautions to protect personal information from loss, misuse and unauthorized access, disclosure, alteration and destruction.

April 12, 2023 66

Data Integrity

Data integrity: Personal information must be relevant for the purposes for which it is to be used. An organization should take reasonable steps to ensure that data is reliable for its intended use, accurate, complete, and current.

April 12, 2023 67

Enforcement Enforcement: In order to ensure compliance with the safe

harbor principles, there must be (a) readily available and affordable independent recourse mechanisms so that each individual's complaints and disputes can be investigated and resolved and damages awarded where the applicable law or private sector initiatives so provide; (b) procedures for verifying that the commitments companies make to adhere to the safe harbor principles have been implemented; and (c) obligations to remedy problems arising out of a failure to comply with the principles. Sanctions must be sufficiently rigorous to ensure compliance by the organization. Organizations that fail to provide annual self certification letters will no longer appear in the list of participants and safe harbor benefits will no longer be assured.

April 12, 2023 68

What do YOU think?

Do you think the policy for your web pages is adequately described? Reasonable?

How you would implement privacy as a web designer?

What are your concerns as a web user?