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CS 345A Data Mining Lecture 1 Introduction to Web Mining
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Page 1: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

CS 345AData MiningLecture 1

Introduction to Web Mining

Page 2: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

What is Web Mining?

Discovering useful information from the World-Wide Web and its usage patterns

Page 3: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining v. Data Mining

Structure (or lack of it) Textual information and linkage structure

Scale Data generated per day is comparable to

largest conventional data warehouses Speed

Often need to react to evolving usage patterns in real-time (e.g., merchandising)

Page 4: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining topics

Web graph analysis Power Laws and The Long Tail Structured data extraction Web advertising Systems Issues

Page 5: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining topics

Web graph analysis Power Laws and The Long Tail Structured data extraction Web advertising Systems Issues

Page 6: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Size of the Web

Number of pages Technically, infinite Much duplication (30-40%) Best estimate of “unique” static HTML

pages comes from search engine claims Google = 8 billion(?), Yahoo = 20 billion

Number of web sites Netcraft survey says 72 million sites

(http://news.netcraft.com/archives/web_server_survey.html)

Page 7: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Netcraft survey

http://news.netcraft.com/archives/web_server_survey.html

Page 8: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

The web as a graph

Pages = nodes, hyperlinks = edges Ignore content Directed graph

High linkage 8-10 links/page on average Power-law degree distribution

Page 9: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Structure of Web graph

Let’s take a closer look at structure Broder et al (2000) studied a crawl of

200M pages and other smaller crawls Bow-tie structure

Not a “small world”

Page 10: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Bow-tie Structure

Source: Broder et al, 2000

Page 11: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

What can the graph tell us?

Distinguish “important” pages from unimportant ones Page rank

Discover communities of related pages Hubs and Authorities

Detect web spam Trust rank

Page 12: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining topics

Web graph analysis Power Laws and The Long Tail Structured data extraction Web advertising Systems Issues

Page 13: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Power-law degree distribution

Source: Broder et al, 2000

Page 14: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Power-laws galore

Structure In-degrees Out-degrees Number of pages per site

Usage patterns Number of visitors Popularity

Page 15: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

The Long Tail

Source: Chris Anderson (2004)

Page 16: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

The Long Tail

Shelf space is a scarce commodity for traditional retailers Also: TV networks, movie theaters,…

The web enables near-zero-cost dissemination of information about products Action moves from Hits to Niches

Page 17: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

The Long Tail

More choice necessitates better filters Recommendation engines (e.g., Amazon) How Into Thin Air made Touching the

Void a bestseller In fact, page rank can be seen as a

long tail filter Tapping into the Wisdom of Crowds

Page 18: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining topics

Web graph analysis Power Laws and The Long Tail Structured data extraction Web advertising Systems Issues

Page 19: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Extracting Structured Data

http://www.simplyhired.com

Page 20: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Extracting structured data

http://www.fatlens.com

Page 21: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining topics

Web graph analysis Power Laws and The Long Tail Structured data extraction Web advertising Systems Issues

Page 22: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Searching the Web

Content aggregatorsThe Web Content consumers

Page 23: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Ads vs. search results

Page 24: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Ads vs. search results

Search advertising is the revenue model Multi-billion-dollar industry Advertisers pay for clicks on their ads

Interesting problems What ads to show for a search? If I’m an advertiser, which search terms

should I bid on and how much to bid?

Page 25: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Sidebar: What’s in a name?

Geico sued Google, contending that it owned the trademark “Geico” Thus, ads for the keyword geico couldn’t

be sold to others Court Ruling: search engines can sell

keywords including trademarks No court ruling yet: whether the ad

itself can use the trademarked word(s)

Page 26: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining topics

Web graph analysis Power Laws and The Long Tail Structured data extraction Web advertising Systems Issues

Page 27: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Systems architecture

Memory

Disk

CPU

Machine Learning, Statistics

“Classical” Data Mining

Page 28: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Very Large-Scale Data Mining

Mem

Disk

CPU

Mem

Disk

CPU

Mem

Disk

CPU…

Cluster of commodity nodes

Page 29: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Systems Issues

Web data sets can be very large Tens to hundreds of terabytes

Cannot mine on a single server! Need large farms of servers

How to organize hardware/software to mine multi-terabye data sets Without breaking the bank!

Page 30: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining topics

Web graph analysis Power Laws and The Long Tail Structured data extraction Web advertising Systems Issues

Page 31: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

Web Mining Project

Lots of interesting project ideas If you can’t think of one please come

discuss with us Data and Infrastructure

Webbase data (older Stanford web crawl) Recent web crawl and server courtesy of

Kosmix

Page 32: CS 345A Data Mining Lecture 1 Introduction to Web Mining.

The World-Wide Web

Our modern-day Library of Alexandria

The Web