Web MiningWeb MiningWeb MiningWeb Mining

Based on several presentations found on the web: Sh i Ull T i P dShapiro, Ullman, Terziyan, Pedersen ...

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Wh t i W b Mi i ?Wh t i W b Mi i ?What is Web Mining?What is Web Mining?

Web mining is the use of data mining techniques to automatically discover and extract information automat cally d scover and extract nformat on from Web documents/services

(Et i i 1996 CACM 39(11))(Etzioni, 1996, CACM 39(11))

Web mining aims to discovery useful information or m g m y f f mknowledge from the Web hyperlink structure, page content and usage data.g

(Bing LIU 2007, Web Data Mining, Springer)

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Wh t i W b Mi i ?Wh t i W b Mi i ?What is Web Mining?What is Web Mining?

Motivation / Opportunity

The WWW is huge, widely distributed, global information service d h f h f d centre and, therefore, constitutes a rich source for data mining

Intelligent Web Search

P li ti R d ti E i Personalization, Recommendation Engines

Web-commerce applications

Building the Semantic Web Building the Semantic Web

Web page classification and categorization

News classification and clustering News classification and clustering

Information / trend monitoring

Analysis of online communities

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y

Web and mail spam filtering

Ab d d th it i iAb d d th it i iAbundance and authority crisisAbundance and authority crisis

Liberal and informal culture of content generation and dissemination

Redundancy and non-standard form and content

Millions of qualifying pages for most broad queriesM ll ons of qual fy ng pages for most broad quer es

Example: java or kayaking

N th it ti i f ti b t th li bilit f it No authoritative information about the reliability of a site

Little support for adapting to the background of specific users

Pages added continuously and average page changes in a few weeks

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Diff t f “ l i l” D t Mi i ?Diff t f “ l i l” D t Mi i ?Different from “classical” Data Mining?Different from “classical” Data Mining?

The web is not a relation

Textual information + linkage structure

Usage data is huge and growing rapidly

Google’s usage logs are bigger than their web crawl

Data generated per day is comparable to largest conventional Data generated per day is comparable to largest conventional data warehouses

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Si f th W bSi f th W bSize of the WebSize of the Web

Number of pages Number of pages

11.5 billion indexable pages (http://www.cs.uiowa.edu/~asignori/web-size/ www2005)

Technically, infinite

Because of dynamically generated content

Lots of duplication (30-40%)

Best estimate of “unique” static HTML pages comes from search i l iengine claims

Yahoo = claimed 19.2 billion in Aug 2005

Number of unique web sites

Netcraft survey says 98 million sites

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O t b 2006 W b S SO t b 2006 W b S SOctober 2006 Web Server SurveyOctober 2006 Web Server Survey

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

from from http://www.worldwidewebsize.com/http://www.worldwidewebsize.com/

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A th t tim t th b iA th t tim t th b iAnother way to estimate the web sizeAnother way to estimate the web size

The number of web servers was estimated by samplingand testing random IP address numbers and determining the fraction of such tests that successfully located a web server

The estimate of the average number of pages per server was obtained by crawling a sample of the serversserver was obtained by crawling a sample of the serversidentified in the first experiment

Lawrence, S. and Giles, C. L. (1999). Accessibility of information on the web Nature 400(6740): 107–109

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web. Nature, 400(6740): 107–109.

Web Information RetrievalWeb Information Retrievalf mf m

According to most predictions, the majority of human informationwill be available on the Web in ten??? years

Effective information retrieval can aid in Research: Find all papers about web mining Research: Find all papers about web mining

Health/ Medicine : What could be reason for symptoms of “yellow eyes”, high fever and frequent vomiting

Travel: Find information on the tropical island of St. Lucia

Business: Find companies that manufacture digital signal processors

Entertainment: Find all movies starring Marilyn Monroe during the years 1960 and 1970

Arts: Find all short stories written by Jhumpa Lahiri

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Arts: Find all short stories written by Jhumpa Lahiri

Why is Web Information Retrieval Difficult?Why is Web Information Retrieval Difficult?Why is Web Information Retrieval Difficult?Why is Web Information Retrieval Difficult?

The Abundance Problem (99% of information of no interest to 99% The Abundance Problem (99% of information of no interest to 99% of people) Hundreds of irrelevant documents returned in response to a search p

query

Limited Coverage of the Web (Internet sources hidden behind search interfaces)search interfaces) Largest crawlers cover less than 18% of Web pages

The Web is extremely dynamic The Web is extremely dynamic Lots of pages added, removed and changed every day

Very high dimensionality (thousands of dimensions) Very high dimensionality (thousands of dimensions)

Limited query interface based on keyword-oriented search

Limited cust mizati n t individual users

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Limited customization to individual users

Search LandscapeSearch Landscape 2005pp

Sept 2009Sept 2009

13http://marketshare.hitslink.com/search-engine-market-share.aspx?qprid=4

S h E i W b C O lS h E i W b C O lSearch Engine Web Coverage OverlapSearch Engine Web Coverage Overlap

4 searches were d f d h defined that returned 141 web pages.

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http://www.searchengineshowdown.com/stats/overlap.shtml

W b h b iW b h b iWeb search basicsWeb search basics

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User

Web Results 1 - 10 of about 7,310,000 for miele. (0.12 seconds)

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pp gAll models. Helpful advice. www.best-vacuum.com

Web crawlerpage ] Das Portal zum Thema Essen & Geniessen online unter www.zu-tisch.de. Miele weltweit ...ein Leben lang. ... Wählen Sie die Miele Vertretung Ihres Landes. www.miele.de/ - 10k - Cached - Similar pages

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IndexerSearch

The Web

15Ad indexesIndexes

W b C li B iW b C li B iWeb Crawling BasicsWeb Crawling Basics

Start with a “seed set” of to-visit urls

get next urlto visit urls

get pagevisited urlsWeb

extract urls

visited urlseb

web pages

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C li IC li ICrawling IssuesCrawling Issues

L ad n web servers Load on web servers

E.g., no more than 1 request to the same server every 10 seconds

Insufficient resources to crawl entire web

Visit “important” pages first (pagerank, inlinks …)

How to keep crawled pages “fresh”?

How often do web pages change? What do we mean by freshness?p g g y

Detecting replicated content e.g., mirrors

Use document comparison techniques (java manuals) Use document comparison techniques (java manuals)

Can’t crawl the web from one machine

P ll li i th l

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Parallelizing the crawl

W b Ad ti iW b Ad ti iWeb AdvertisingWeb Advertising Banner ads (1995-2001) Banner ads (1995 2001)

Initial form of web advertising

P p l bsit s h d X$ f 1000 “imp ssi ns” f d Popular websites charged X$ for every 1000 impressions” of ad

Modeled similar to TV, magazine ads

L li kth u h t s Low clickthrough rates

low ROI for advertisers

I t d d b O t d 2000 Introduced by Overture around 2000

Advertisers “bid” on search keywords

When someone searches for that keyword, the highest bidder’s ad is shown

Advertiser is charged only if the ad is clicked on

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Advertiser is charged only if the ad is clicked on

W b Ad ti iW b Ad ti iWeb AdvertisingWeb Advertising

Search advertising is the revenue model

Multi-billion-dollar industry Multi-billion-dollar industry

Advertisers pay for clicks on their ads

Interesting problems

What ads to show for a search? What ads to show for a search?

Maximise revenue, each advertiser has a limited budget

If I’m an advertiser, which search terms should I bid on and how much to bid?

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Web Mining TaxonomyWeb Mining TaxonomyWeb Mining TaxonomyWeb Mining Taxonomy

Web Mining

Web S

Web C Web UsageStructure

MiningContentMining

Web UsageMining

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Web Mining TaxonomyWeb Mining TaxonomyWeb Mining TaxonomyWeb Mining Taxonomy Web content mining: focuses on techniques for

assisting a user in finding documents that meet a certain criterion

Web structure mining: aims at developing techniques to Web structure mining: aims at developing techniques to take advantage of the collective judgement of web page quality which is available in the form of hyperlinksquality which is available in the form of hyperlinks

Web usage mining: focuses on techniques to study the user behaviour when navigating the web(also known as Web log mining and clickstream analysis)

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W b C t t Mi iW b C t t Mi iWeb Content MiningWeb Content Mining

Examines the content of web pages as well as results of web searching.

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W b C t t MiW b C t t MiWeb Content MinngWeb Content Minng

Can be thought of as extending the work performed by basic search engines

Search engines have crawlers to search the web and ggather information, indexing techniques to store the information, and query processing support to provide q y p g pp pinformation to the users

Web Content Mining is: the process of extracting knowledge from web contents

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g

I f m ti R t i lI f m ti R t i lInformation RetrievalInformation Retrieval

Given:

A source of textual

Documentssource

documents

A user query (text based) IRQueryq y ( ) IRSystem

Fi d Find:

A set (ranked) of documents Document

DocumentD tthat are relevant to the

queryRanked

DocumentsDocument

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S miS mi St t d D tSt t d D tSemiSemi--Structured DataStructured Data

Text content is in general semi structured Text content is, in general, semi-structured

Example:

Title

A th Author

Publication_Date Structured attribute/value pairs

Length

Category Category

AbstractUnstructured

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Content

Structuring Textual InformationStructuring Textual InformationStructuring Textual InformationStructuring Textual Information

Many methods designed to analyze structured data

If we can represent documents by a set of attributes we will be able to use existing data mining methods

How to represent a document?

Vector based representation p

(referred to as “bag of words” as it is invariant to permutations)

Use statistics to add a numerical dimension to unstructured text

Term frequencyf q y

Document frequency

Term proximity

26Document length

Term proximity

Document RepresentationDocument RepresentationDocument RepresentationDocument Representation

A document representation aims to capture what the document m p m p mis about

One possible approach (boolean representation):

Each entry describes a document

Attribute describe whether or not a term appears in the ddocument

Example Terms

Camera Digital Memory Pixel …

Document 1 1 1 0 1Document 1 1 1 0 1

Document 2 1 1 0 0

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… … … … …

Document RepresentationDocument RepresentationDocument RepresentationDocument Representation

Another approach:

Each entry describes a document

Attributes represent the frequency in which a term appears in the document

Example: Term frequency table

Terms

Camera Digital Memory Print …

Document 1 3 2 0 1

Document 2 0 4 0 3

28… … … … …

Document RepresentationDocument RepresentationDocument RepresentationDocument Representation

But a term is mentioned more times in longer documents

Th f l ti f (% f d t) Therefore, use relative frequency (% of document):

No. of occurrences/No. of words in document

Terms

Camera Digital Memory Print …

Document 1 0.03 0.02 0 0.01

Document 2 0 0.004 0 0.003

29… … … … …

More on Document RepresentationMore on Document RepresentationMore on Document RepresentationMore on Document Representation

Stop Word removal: Many words are not informative and thus Stop Word removal: Many words are not informative and thus irrelevant for document representation

the, and, a, an, is, of, that, …th , an , a, an, s, of, that, …

Stemming: reducing words to their root form (Reduce dimensionality)

A document may contain several occurrences of words like A document may contain several occurrences of words like

fish, fishes, fisher, and fishers

But would not be retrieved by a query with the keyword y q y y

fishing

Different words share the same word stem and should be represented with its stem, instead of the actual word

Fish

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For the Portuguese language these techniques are less studied

Weighting Scheme for Term FrequenciesWeighting Scheme for Term FrequenciesWeighting Scheme for Term FrequenciesWeighting Scheme for Term Frequencies TF-IDF weighting: give higher weight to terms that are rareTF IDF weighting give higher weight to terms that are rare

TF: term frequency (increases weight of frequent terms)

If a term is frequent in lots of documents it does not have discriminative power If a term is frequent in lots of documents it does not have discriminative power

IDF: inverse term frequency

ijij

ij

dwndw

document in of soccurrence of number the is document and term given a For

i

ijij d

nTF

i

ijij

nd

w

documents of number the is document in words of number the is d

umfufum

i

nn

IDF jj log

jj w n contain that documents of number the is jijij IDFTFx

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There is no compelling motivation for this method but it has been shown to be superior to other methods

Locating Relevant DocumentsLocating Relevant DocumentsLocating Relevant DocumentsLocating Relevant Documents

Given a set of keywords

Use similarity/distance measure to find similar/relevant documents

Rank documents by their relevance/similarity

H t d t i if t d t i il ?How to determine if two documents are similar?

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Di t B d M t hiDi t B d M t hi In order retrieve documents similar to a given document we need a

Distance Based MatchingDistance Based Matching

measure of similarity

Euclidean distance (example of a metric distance):

The Euclidean distance between

X=(x1, x2, x3,…xn) and Y =(y1,y2, y3,…yn) X (x1, x2, x3,…xn) and Y (y1,y2, y3,…yn)

is defined as:

n B C

n

iii yxYXD

1

2)(),(B C

AProperties of a metric distance:

• D(X,X)=0• D(X Y)=D(Y X)

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D• D(X,Y)=D(Y,X)• D(X,Z)+D(Z,Y) ≥ D(X,Y)

Angle Based MatchingAngle Based MatchingAngle Based MatchingAngle Based Matching Cosine of the angle between the vectors representing the document

and the query

Documents “in the same direction” are closely related.y

Transforms the angular measure into a measure ranging from 1 for the highest similarity to 0 for the lowestthe highest similarity to 0 for the lowest

B CT

T

YXYXYXYXD ),cos(),(

A

D

22

ii

ii

yxyx

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D

Performance MeasurePerformance Measure

The set of retrieved documents can be formed by collecting the top-ranking documents according to a similarity measure

The quality of a collection can be compared by the two following measures

}{}{}{

RetrievedRetrievedRelevant

precision

percentage of retrieved documents that are in fact relevant to the query (i.e., “correct” responses)

}{}{}{

}{

RelevantRetrievedRelevant

recall

Retrieved

percentage of documents that are relevant to the query and were, in fact, retrieved

}{

Retrieveddocuments

Relevantdocuments

Relevant &retrieved

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All documents

I t lli t W b S hI t lli t W b S hIntelligent Web SearchIntelligent Web Search

Combine the intelligent IR tools

meaning of words meaning of words

order of words in the query

authority of the source

With the unique web features

retrieve Hyper-link information

utilize Hyper-link as input

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yp p

Text MininText MininText MiningText Mining Data mining in text: find something useful and surprising from a

text collection;t t i i i f ti t i l text mining vs. information retrieval;

data mining vs. database queries.

D t l ifi ti Document classification Topic hierarchies, spam filters

l Document clustering cluster documents by a common author

cluster documents containing information from a common source (fraud)

Key-word based association rules

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Key-word based association rules

Clustering and automatic automatic clusters’ labeling

38http://clusty.com

39 40

Web Structure MiningWeb Structure Mining

Exploiting Hyperlink Structure

Social network analysis

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Fi t ti f h iFi t ti f h iFirst generation of search enginesFirst generation of search engines

E l d k d b d h Early days: keyword based searches

Keywords: “web mining”

Retrieves documents with “web” and mining”

L t ith Later on: cope with

synonymy problem

polysemy problem

stop words stop words

Common characteristic: Only information on the

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pages is used

M d h i M d h i Modern search engines Modern search engines

Link structure is very important

Adding a link: deliberate act Adding a link: deliberate act

Harder to fool systems using in-links

Link is a “quality mark”

A page is important if important pages link to itp g p p p g

M d h l k Modern search engines use link structure as important source of information

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C t l Q tiCentral Question:

Which useful information can be Which useful information can be Wh ch u fu nf rmat n can Wh ch u fu nf rmat n can derived derived

f th li k t t f th b?f th li k t t f th b?from the link structure of the web?from the link structure of the web?

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Some answersSome answersSome answersSome answers

1. Structure of Internet1. Structure of Internet

2. Google2. Google

3. HITS: Hubs and Authorities3. HITS Hubs and Authorities

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1 Th W b St t 1 Th W b St t 1. The Web Structure 1. The Web Structure

A study was conducted on a graph inferred from two large Altavista crawls.

Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., andWiener, J. (2000). Graph structure in the web. In Proc. WWW Conference.

Th st d fi m d th h p th sis th t th mb f The study confirmed the hypothesis that the number of in-links and out-links to a page approximately follows a Zipf distribution (a particular case of a power law)Zipf distribution (a particular case of a power-law)

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P LP LPower LawsPower Laws

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II Li kLi kInIn--LinksLinks

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O tO t Li kLi kOutOut--LinksLinks

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Th W b St t Th W b St t The Web Structure The Web Structure

If the web is treated as an undirected graph

90% of the pages form a single connected componentcomponent

If the web is treated as a directed graphIf the web is treated as a directed graph

four distinct components are identified, the four p ,with similar size

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General TopologyGeneral Topology

TendrilsTendrils44mil

SCCIN OUTSCCIN44mil 44mil56mil

DisconnectedTubes Disconnected components

Tubes

SCC: set of pages that can be reached by one anotherIN: pages that have a path to SCC but not from it

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IN: pages that have a path to SCC but not from itOUT: pages that can be reached by SCC but not reach itTENDRILS: pages that cannot reach and be reached the SCC pages

Some statisticsSome statistics Only between 25% of the pages there is a connecting path

BUT

If there is a path: If there is a path:

Directed: average length <17

U di t d l th 7 (!!!) Undirected: average length <7 (!!!)

It’s a “small world” -> between two people only chain of length 6!(http://en.wikipedia.org/wiki/Small_world_phenomenon)

Small World Graphs

High number of relatively small cliques

Small diameter

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Internet (SCC) is a small world graph

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GoogleGoogleGoogleGoogle Search engine that uses link structure to calculate a g

quality ranking (PageRank) for each page

I t iti P R k b th b bilit th t Intuition: PageRank can be seen as the probability that a “random surfer” visits a page Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. In Proc. WWW Conference, pages 107–117

A page is important if important pages link to it

54http://paspespuyas.com/comunidad/media/pageRank.gif

G lG lGoogleGoogle

Keywords w entered by user

Select pages containing w and pages which have in-links Select pages containing w and pages which have in links with caption w Anch r t xt Anchor text

Provides more accurate descriptions of Web pages

Anchors exist for un-indexable documents (e.g., images)

Font sizes of words in text:

Words in larger or bolder font are assigned higher weights

Rank pages according to importance

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p g g mp

PageRankPageRankPageRankPageRank(P R nk) + (W bsit C nt nt) Ov r ll R nk in R sults

Page RankPage Rank:: A page is important if many important pages link to it.

(PageRank) + (Website Content) = Overall Rank in Results

Link ij :

ag anag an p g mp f m y mp p g .

j i considers j important. the more important i, the more important j becomes. if i has many out-links: links are less important.

Initially: all importances pi = 1. Iteratively, pi is refined.

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ji iOutDegree

iPageRankppjPageRank)(

)()()( 1

PageRankPageRank

Let OutDegreei = # out-links of page i

Adjust pj:j pj

ji iOutDegree

iPageRankppjPageRank)(

)()()( 1

h h h h h

ji g )(

This is the weighted sum of the importance of the pages referring to PjParameter p is probability that the surfer gets bored and starts on a new random pagep g(1-p) is the probability that the random surfer follows a link on current page

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P R kP R kPageRankPageRank

58Repeat until pagerank vector converges…

HITS HITS (Hyperlink(Hyperlink--Induced Topic Search)Induced Topic Search)HITS HITS (Hyperlink(Hyperlink Induced Topic Search)Induced Topic Search)

HITS uses hyperlink structure to identify authoritative Web sources for broad-topic information discovery

Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604–632.

Premise: Sufficiently broad topics contain communities consisting of two types of hyperlinked pages:g yp yp p g

Authorities: highly-referenced pages on a topic

H b th t “ i t” t th iti Hubs: pages that “point” to authorities

A good authority is pointed to by many good hubs; a good hub d h

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points to many good authorities

HubsHubsHubsHubs

Pages that link to a collection of authoritative pages on a broad topicpages point to interesting links to authorities = relevant pages

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AuthoritiesAuthoritiesAuthoritiesAuthorities

Relevant pages of the highest quality on a broad topicRelevant pages of the highest quality on a broad topic

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HITSHITS Steps for Discovering Hubs and Authorities on a

specific topicspecific topic Collect seed set of pages S (returned by search engine)

Expand seed set to contain pages that point to or are pointedto by pages in seed set (removes links inside a site)

Iteratively update hub weight h(p) and authority weight a(p) for each page:

qppq

qaphqhpa )()()()(

After a fixed number of iterations, pages with highest hub/authority weights form core of community

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hub/authority weights form core of community

St th d k f HITS St th d k f HITS Strengths and weaknesses of HITS Strengths and weaknesses of HITS

Strength: its ability to rank pages according to the Strength: its ability to rank pages according to the query topic, which may be able to provide more relevant authority and hub pages relevant authority and hub pages.

Weaknesses:

It is easily spammed. It is in fact quite easy to influence HITS since adding out-links in one’s own page is so easy. g p g y

Topic drift. Many pages in the expanded set may not be on topic. p

Inefficiency at query time: The query time evaluation is slow. Collecting the root set, expanding it and performing

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slow. Collecting the root set, expanding it and performing eigenvector computation are all expensive operations

Web Usage MiningWeb Usage Miningg gg g

l i b i tianalyzing user web navigation

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Web Usage MiningWeb Usage MiningWeb Usage MiningWeb Usage Mining

Pages contain information

Links are “roads” Links are roads

How do people navigate over the Internet?

Web usage mining (Clickstream Analysis)

Information on navigation paths is available in log files Information on navigation paths is available in log files.

Logs can be examined from either a client or a server

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perspective.

W b it U A l iW b it U A l iWebsite Usage AnalysisWebsite Usage Analysis

Why analyze Website usa e? Why analyze Website usage?

Knowledge about how visitors use Website could

Provide guidelines to web site reorganization; Help prevent disorientation

Help designers place important information where the visitors look for it

Pre-fetching and caching web pages

Provide adaptive Website (Personalization)

Questions which could be answered

What are the differences in usage and access patterns among users?

What user behaviours change over time?

How usage patterns change with quality of service (slow/fast)?

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What is the distribution of network traffic over time?

Website Usage AnalysisWebsite Usage Analysis

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Data SourcesData SourcesData SourcesData Sources

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D t SD t SData SourcesData Sources

Server level collection: the server stores data regarding requests performed by the client, thus data regard generally just one source;

Client level collection: it is the client itself which sends to a f wrepository information regarding the user's behaviour (can be implemented by using a remote agent (such as Javascripts or Java applets) or by modifying the source code of an existing browser pp ) y y g g(such as Mosaic or Mozilla) to enhance its data collection capabilities. );

Proxy level collection: information is stored at the proxy side, thus Web data regards several Websites, but only users whose

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g , yWeb clients pass through the proxy.

W b U Mi i PW b U Mi i PWeb Usage Mining ProcessWeb Usage Mining Process

WebSServer

Log DataPreparation Clean Data

Mi iPreparation Data Mining

SitSiteData Usage

Patterns

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D t P tiD t P tiData PreparationData Preparation Data cleaningData cleaning

By checking the suffix of the URL name, for example, all log entries with filename suffixes such as, gif, jpeg, etc, g , jp g,

User identification

If p is st d th t is n t di tl link d t th p i s p s If a page is requested that is not directly linked to the previous pages, multiple users are assumed to exist on the same machine

Other heuristics involve using a combination of IP address machine Other heuristics involve using a combination of IP address, machine name, browser agent, and temporal information to identify users

Transaction identification Transaction identification

All of the page references made by a user during a single visit to a site

Si f t ti f i l f t ll f

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Size of a transaction can range from a single page reference to all of the page references

A l A l W b L Fil A lW b L Fil A lAnalog Analog –– Web Log File AnalyserWeb Log File Analyserhttp://www.analog.cx/

Gives basic statistics such as number of hits number of hits average hits per time period what are the popular pages in your site what are the popular pages in your site who is visiting your site what keywords are users searching for to get to you what keywords are users searching for to get to you what is being downloaded

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W b U Mi iW b U Mi iWeb Usage MiningWeb Usage Mining

Commonly used approaches

Preprocessing data and adapting existing data mining techniques

For example associatin rules: does not take into account the order of the page requestsorder of the page requests

Developing novel data mining modelsp g g

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Data Mining on Web TransactionsData Mining on Web TransactionsData M n ng on W ransact onsData M n ng on W ransact ons

Association Rules: discovers similarity among sets of items across transactions

X =====> Y

where X, Y are sets of items, confidence or P(X v Y),

support or P(X^Y)

Examples: 60% of clients who accessed /products/, also accessed

/products/software/webminer.htm. 30% of clients who accessed /special-offer.html, placed an online order in

/products/software/.

(Actual Example from IBM official Olympics Site)

{Badminton Diving} ===> {Table Tennis} (69 7% 35%)

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{Badminton, Diving} ===> {Table Tennis} (69.7%,.35%)

Other Data Mining TechniquesOther Data Mining Techniques

Sequential Patterns:

30% of clients who visited /products/software/, had done a search in Yahoousing the keyword “software” before their visit

60% of clients who placed an online order for WEBMINER, placed another online order for software within 15 days

Clustering and Classification Clustering and Classification

clients who often access /products/software/webminer.html tend to be from educational institutions.from educational institutions.

clients who placed an online order for software tend to be students in the 20-25 age group and live in the United States.

75% of clients who download software from /products/software/demos/ visit between 7:00 and 11:00 pm on weekends.

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Path and Usage Pattern DiscoveryPath and Usage Pattern Discoveryath an Usag att rn D sco ryath an Usag att rn D sco ry

Types of Path/Usage Information

Most Frequent paths traversed by users

Entry and Exit Points

Distribution of user session duration

Examples: Examples:

60% of clients who accessed /home/products/file1.html, followed the path /home ==> /home/whatsnew ==> /home/productsfollowed the path /home > /home/whatsnew > /home/products==> /home/products/file1.html

(Olympics Web site) 30% of clients who accessed sport specific pages( y p ) p p p gstarted from the Sneakpeek page.

65% of clients left the site after 4 or less references.

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A Fi l Q tiA Fi l Q tiA Final QuestionA Final Question

Google Tools: search engine, Google news, Google maps, Gmail, Calendar, Google docs, YouTube, Orkut, Google Desktop, Chrome, Android...

Do You Trust Google to Resist Data Mining Across Services?

http://www readwriteweb com/archives/do you trust http://www.readwriteweb.com/archives/do_you_trust_google_to_resist_data_mining_across_services.php

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S mmS mmSummarySummary

Web is huge and dynamic

Web mining makes use of data mining techniques to Web mining makes use of data mining techniques to automatically discover and extract information from Web documents/services Web documents/services

Web content mining

Web structure mining

Web usage mining Web usage mining

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R fR fReferencesReferences

Data Mining: Introductory and Advanced Topics, Margaret Dunham (Prentice Hall, 2002)

Mining the Web - Discovering Knowledge from Hypertext Data Soumen Chakrabarti MorganHypertext Data, Soumen Chakrabarti, Morgan-Kaufmann Publishers

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Thank you !!!Thank you !!!80

Thank you !!!Thank you !!!