รหั�สวิ�ชา 273383การทำ�าเหัมื�องข้�อมื�ลเวิ�ป

(Web Mining)

สาขาเทคโนโลยี�สารสนเทศคณะเทคโนโลยี�สารสนเทศและการส��อสาร

ค�าอธิ�บายีรายีวิ�ชา (Course Description)

หล�กการเบ��องต้ นการท�าเหมื�องข อมื"ล สถาปั%ต้ยีกรรมื ของเวิ&ปั สถาปั%ต้ยีกรรมืของโปัรแกรมืค นหา การท�า

เหมื�องโครงสร างเวิ&บและการวิ�เคราะห'เช��อมืโยีง เท คน�คครอวิ'ล�ง การค นหาและการท�าดรรชน� การท�า

เหมื�องเวิ&บคอนเท&น การวิ�เคราะห'แฟ้*มืลงบ�นท+กเข า ออกบนเซิ�ร'ฟ้เวิอร' และการปัระยี-กต้'ใช

Web mining concept, Web architecture, Searching program architecture, Web structure mining, Link analysis, crawling technique, Indexing and searching, web mining content, Server logs, and implementation

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แนะน�าวิ�ชา

ดร.ส-ขชาต้ร� ปัระสมืส-ข (Sukchatri PRASOMSUK, Ph.D.) PhD. (Computational Linguistics), INALCO, Paris, FR M.Eng.(IT), & Grad.Dip. In Applied IS, RMIT, Melbourne, AU B.Sc.(คณิ�ตศาสตร�), มื.รามืค�าแหัง เคยทำ�างานทำ� :

DATA SOLVE Co.,Ltd. & Central Trading Co.,Ltd กรมืวิ�ทำยาศาสตร�บร�การ กระทำรวิงวิ�ทำย�ฯ มื.หัอการค�า และ มื.แมื%ฟ้'าหัลวิง

Contact : skchatri@hotmail.com FaceBook : Sukchatri PSK 0804509105

Notes/Slides Download: http://www.ict.up.ac.th/skchatri/

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แนะน�าอาจารย�และน�ส�ต

เกณฑ์'การวิ�ดผลและปัระเมื�นผล (Evaluation criteria) เกณฑ์'การวิ�ดผล 1.1. ภาคบรรยีายี รวิมื 70% - การสอบกลางภาค/ปลายภาค 35% + 35% 1.2. ภาคทดลอง/ภาคปัฏิ�บ�ต้�การ รวิมื 30%

การเข้�าเร�ยน 10 %

จ�ตพิ�ส�ยและงานทำ�มือบหัมืาย 10 %

LAB 20 %

รวิมืท��งหมืด 100 % เกณฑ์'การต้�ดเกรด

ใช�เกณิฑ์�การต�ดเกรดแบบอ�งเกณิฑ์�/อ�งกล.%มื อ�งเกณิฑ์� < 50 คะแนน ได�เกรด F

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การประเมื�นผลการเร�ยน

ระบบการจ�ดเก�บและการส�บค�นสารสนเทำศด�วิยคอมืพิ�วิเตอร�, พิ�ทำ�กษ์�การพิ�มืพิ�, พิ�มืพิ�คร�2งทำ� 2, ดร.ศ.ภช�ย ต�2งวิงศ�ศานต�, 2553

An Introduction to Information Retrieval, Online edition (c)2009 Cambridge UP, Draft of April 1, 2009

Data Mining, Practical Machine Learning Tools and Techniques, Third Edition, Ian H. Witten Eibe Frank Mark A. Hall, Elsevier, 2011

Web Mining and Social Networking, Techniques and Applications, Series Editor: Yanchun Zhang, Victoria University, Australia, Springer, 2011

Web mining : applications and techniques / Anthony Scime, Editor, State University of New York College at Brockport, USA, Idea Group Inc., 2005

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เอกสารปัระกอบการสอน(Teaching Materials / References)

การต�ดต%อได�ทำ�2งทำาง e-mail หัร�อ โทำรศ�พิทำ� หัร�อเข้�าพิบทำ� หั�องพิ�ก

E-mail : แจ�ง ช� อ- นามืสก.ล รหั�ส เร� องทำ� จะ ต�ดต%อ ค�าข้อบค.ณิป4ดทำ�าย

Mobile Phone : แจ�ง ช� อ- นามืสก.ล น�ส�ตวิ�ชา เร� องทำ� จะต�ดต%อ ค�าข้อบค.ณิเมื� อพิ�ดธุ.ระเสร�จ

การเข้�าพิบทำ� หั�องพิ�กอาจารย� : แจ�งการเข้�าพิบโดย การน�ดทำาง e-mail หัร�อ โทำรมืาน�ด วิ�นและเวิลา

ก%อนการเข้�าพิบเตร�ยมืเร� องข้องตนเองใหั�พิร�อมื การเตร�ยมืและล�าด�บค�าถามืใหั�พิร�อมื พิ�ดภาษ์าไทำย

ใหั�ช�ดเจนWeb Mining6

มืารยาทำในการต�ดต%ออาจารย�

ป4ดมื�อถ�อ อยี4า- ค.ยก�บเพิ� อน ส%งเส�ยงด�ง ค.ยโทำรศ�พิทำ� เล%นมื�อถ�อ หัร�อคอมืพิ�วิเตอร� หัร�อ อาการทำ� ไร�มืารยาทำในหั�องบรรยายเช%น

…นอนหัล�บ เตร�ยมืต�วิเสมือ พิร�อมืทำ� จะตอบค�าถามื หัร�อถามื (โดยการยกมื�อ) ไมื%ก�นน�2าหัร�ออาหัารข้ณิะมื�บรรยาย ไมื%ล.กออกไปจากหั�อง โดยไร�มืารยาทำ (อาจารย�มื�เวิลาพิ�กใหั�เป7นระ

ยะๆ) ข้ณิะเพิ� อนหัร�ออาจารย�ก�าล�งบรรยายหัร�อพิร�เซนต� ควิรใหั�เกร�ยต�

อาจารย�หัร�อเพิ� อน โดยการต�2งใจฟ้:งอย%างมื�มืารยาทำ ต�2งใจเร�ยนด�วิยควิามืสนใจ (อาจารย�จะด�พิฤต�กรรมืข้องน�ส�ต

ตลอดเพิ� อหั�กคะแนน) เพิราะเวิลาเร�ยนเป7นเวิลาทำ� มื�ค%าอย%างส�ง

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มืารยาทำในหั�องเร�ยนข้ณิะบรรยายหัร�อเร�ยน

ข้ณิะเร�ยน เมื� ออาจารย�ถามื ตอบได�เสมือ ไมื%ไปถามืเพิ� อนข้�างๆ มื�ควิามืประพิฤต�ในหั�องเร�ยนด�ตลอด เข้�าเร�ยนสมื� าเสมือ ควิามื

ต�2งใจด� ส%งงานทำ� มือบหัมืายอย%างสมื� าเสมือ จ�าค�าศ�พิทำ�ทำ� เป7นภาษ์าอ�งกฤษ์ประกอบด�วิย เพิ� อประโยชน�ในการ

สอบ เวิลาสอบ การทำ�าข้�อสอบ : กรณิ�เป7นข้�อเข้�ยนหัร�ออ�ตน�ย (เป7น

เทำคน�คในการตอบค�าถามื ใช�ได�ก�บทำ.กวิ�ชา ทำ� วิโลก) เข้�ยนหัร�ออธุ�บายใหั�ได�ใจควิามืมืากทำ� ส.ด (ถ�ามื�ศ�พิทำ�เทำคน�คเป7นภาษ์า

อ�งกฤษ์ต�องเข้�ยนก�าก�บมืาด�วิย) ยกต�วิอย%าง (ถ�ามื�) เข้�ยนภาพิหัร�อแผนผ�งประกอบ (ถ�ามื�)

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วิ�ธุ�และเทำคน�คในการทำ�าและร�กษ์าคะแนน

An introduction to Web Mining

Source : Bettina Berendt, K.U. Leuven, Belgium, : www.berendt.de

Review/Present by Dr.Sukchatri Prasomsuk,

IT. ICT, University of Phayao, ThailandWeb Mining9

Web mining - is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web usage mining, Web content mining and Web structure mining.

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Definition

Web usage mining  Web structure mining Web content mining

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Main topic of web mining

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Web mining structure

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Web mining structure

Using of Web Mining

Web Mining is the use of the data mining techniques to automatically discover and extract information from web documents/services

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

Using data mining techniques to make the web more useful and more profitable (for some) and to increase the efficiency of our interaction with the web

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Web Mining

Data Mining Techniques Association rules Sequential patterns Classification Clustering Outlier discovery

Applications to the Web E-commerce Information retrieval (search) Network management

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Web Mining

The WWW is huge, widely distributed, global information service centre for Information services: news, advertisements,

consumer information, financial management, education, government, e-commerce, etc.

Hyper-link information Access and usage information

WWW provides rich sources of data for data mining

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Why Mine the Web? Enormous wealth of information on Web

Financial information (e.g. stock quotes) Book/CD/Video stores (e.g. Amazon) Restaurant information (e.g. Zagats) Car prices (e.g. Carpoint)

Lots of data on user access patterns Web logs contain sequence of URLs accessed by

users

Possible to mine interesting nuggets of information People who ski also travel frequently to Europe Tech stocks have corrections in the summer and rally

from November until FebruaryWeb Mining17

Why is Web Mining Different?

The Web is a huge collection of documents except for Hyper-link information Access and usage information

The Web is very dynamic New pages are constantly being generated

Challenge: Develop new Web mining algorithms and adapt traditional data mining algorithms to Exploit hyper-links and access patterns Be incremental Web Mining18

Web Mining Applications

E-commerce (Infrastructure) Generate user profiles Targetted advertizing Fraud Similar image retrieval

Information retrieval (Search) on the Web Automated generation of topic hierarchies Web knowledge bases Extraction of schema for XML documents

Network Management Performance management Fault management

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Problems with Web Search Today

Today’s search engines are plagued by problems: the abundance problem (99% of info of no

interest to 99% of people) limited coverage of the Web (internet sources

hidden behind search interfaces)Largest crawlers cover < 18% of all web pages

limited query interface based on keyword-oriented search

limited customization to individual users

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Problems with Web Search Today

Today’s search engines are plagued by problems: Web is highly dynamic

Lot of pages added, removed, and updated every day

Very high dimensionality

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Improve Search By Adding Structure to the Web

Use Web directories (or topic hierarchies) Provide a hierarchical classification of documents (e.g., Yahoo!)

Searches performed in the context of a topic restricts the search to only a subset of web pages related to the topic

Recreation Science Business News

Yahoo home page

SportsTravel Companies Finance Jobs

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Network Management Objective: To deliver content to users quickly

and reliably Traffic management Fault management

Service Provider NetworkRouter

Server

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Why is Traffic Management Important?

While annual bandwidth demand is increasing ten-fold on average, annual bandwidth supply is rising only by a factor of three

Result is frequent congestion at servers and on network links

during a major event (e.g., princess diana’s death), an overwhelming number of user requests can result in millions of redundant copies of data flowing back and forth across the world

Olympic sites during the games NASA sites close to launch and landing of shuttles

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Traffic Management

Key Ideas

Dynamically replicate/cache content at multiple sites within the network and closer to the user

Multiple paths between any pair of sites

Route user requests to server closest to the user or least loaded server

Use path with least congested network links

Akamai, Inktomi

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Traffic Management Need to mine network and Web traffic to determine

What content to replicate? Which servers should store replicas? Which server to route a user request?

What path to use to route packets?

Network Design issues Where to place servers? Where to place routers? Which routers should be connected by links?

One can use association rules, sequential pattern mining algorithms to cache/prefetch replicas at server

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Web Mining Issues

Size Grows at about 1 million pages a day Google indexes 9 billion documents Number of web sites

Netcraft survey says 72 million sites(http://news.netcraft.com/archives/web_server_survey.html)

Diverse types of data Images Text Audio/video XML HTML

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Number of Active Sites

Total Sites Across All Domains August 1995 - October 2007Web Mining28

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!

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Different Data Formats Structured Data Unstructured Data OLE DB (Object Linking and Embedding,

Database) offers some solutions!

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Web Data

Web pages Intra-page structures Inter-page structures Usage data Supplemental data

Profiles ข้�อมื�ลรายละเอ�ยด Registration information ข้�อมื�ลการลง

ทำะเบ�ยน Cookies

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Web Usage Mining

Pages contain information Links are ‘roads’ How do people navigate the

Internet Web Usage Mining (clickstream

analysis) Information on navigation paths

available in log files Logs can be mined from a client or

a server perspectiveWeb Mining32

Website Usage Analysis

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 การด<งข้�อมื�ลล%วิงหัน�าและแคชหัน�าเวิ�บ

Provide adaptive Website (Personalization) ใหั�เวิ�บไซต�ทำ� ปร�บแต%งได�

Questions which could be answered What are the differences in usage and access patterns among

users? What user behaviors change over time? How usage patterns change with quality of service (slow/fast)? What is the distribution of network traffic over time?

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Website Usage Analysis

Analog – Web Log File AnalyserGives basic statistics such as

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

http://www.analog.cx/

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Web Usage Mining Process

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Web Mining Outline

Goal: Examine the use of data mining on the World Wide Web

Web Content Mining Web Structure Mining Web Usage Mining

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Web Mining Taxonomy

Modified from [zai01]

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Web Content Mining Examine the contents of web pages as well as

result of web searching Can be thought of as extending the work

performed by basic search engines Search engines have crawlers to search the

web and gather information, indexing techniques to store the information, and query processing support to provide information to the users

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

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Semi-structured Data

Content is, in general, semi-structured Example:

Title Author Publication_Date Length Category Abstract Content

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Structuring Textual Data

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

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

Use statistics to add a numerical dimension to unstructured text

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Document Representation

A document representation aims to capture what the document is about

One possible approach: Each entry describes a document Attribute describe whether or not a term appears in

the document

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Document Representation

Another approach:• Each entry describes a document• Attributes represent the frequency in which a term appears in the

document

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Document Representation

• Stop Word removal: Many words are not informative and thus

irrelevant for document representationthe, and, a, an, is, of, that, …

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

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 “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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Web structure mining is the process of using graph theory to analyze the node and connection structure of a web site. According to the type of web structural data, web structure mining can be divided a into two kinds:

1. Extracting patterns from hyperlinks in the web: a hyperlink is a structural component that connects the web page to a different location.

2. Mining the document structure: analysis of the tree-like structure of page structures to describe HTML or XML tag usage.

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Web Structure Mining

Web usage mining is the process of extracting useful information from server logs e.g. users' history.

Web usage mining is the process of finding out what users are looking for on the Internet. 

Web Server Data: The user logs are collected by the Web server. 

Application Server Data: Commercial application servers have significant features to enable e-commerce applications to be built on top of them with little effort.

Application Level Data: New kinds of events can be defined in an application, and logging can be turned on for them thus generating histories of these specially defined events.

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Web Usage Mining

The start point:Key notions of Information Retrieval Representation, storage, organization of, and access to

information items Focus is on the user information need

User information need example:Find all docs containing information on college tennis teams which: (1) are maintained by a USA university and (2) participate in the NCAA tournament.

Information retrieval information about a subject or topic semantics is frequently loose small errors are tolerated

IR system: interpret contents of information items generate a ranking which reflects relevance notion of relevance is most important

Retrieval

Browsing

Database

[from Berthier Ribeiro-Neto’s slides for the Baeza/Ribeiro-Neto IR book]

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IR and KD

Information Retrieval (IR)

Knowledge Discovery * (KD)

*(better term for data mining)

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IR and KD: Different ways of utilizing databases (DBs) IR: „retrieving the information from a DB that matches a user‘s information

need“

query (formal statement of information need) object (an entity which stores information in a database)

KD: „finding new knowledge about the real-world entities described in a DB“

data/information (sometimes plus query) patterns („knowledge“)

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IR and KD: confluences

Conceptually: IR can be seen as a classification of objects

to the classes „relevant to the user‘s query“ / „not relevant to the user‘s query“

(and classification is a typical KD task) KD needs to extract the information from

objects like documents, in order to find new knowledge

(and information extraction is a typical IR task)

Pragmatically: e.g. overlaps of topics and techniques in

papers at SIGIR, SIGKDDWeb Mining49

Web Mining Knowledge discovery

(aka Data mining):“the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” 1

Web Mining: the application of data mining techniques on the content, (hyperlink) structure, and usage of Web resources. Web mining areas:

Web content mining

Web structure mining

Web usage mining

1 Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.) (1996). Advances in Knowledge Discovery and Data Mining. Boston, MA: AAAI/MIT Press

Navigation, queries, content access & creation

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What‘s different about Web mining (different from data mining in general)?

The data and the necessary data preparation steps

To some extent, the applicable techniques

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Other application areas of knowledge discovery

Customer relationship management Finance Banking / Credit Scoring and Insurance Healthcare Bioinformatics (e.g., genomics) Impact of genetically modified

organisms Texts (on- or offline) ...

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The process part of knowledge discovery

CRISP-DM • CRoss Industry Standard Process for Data Mining• a data mining process model that describes commonly used

approaches that expert data miners use to tackle problems.Web Mining53

The structural/algorithmic part of knowledge discovery (“modelling“ in CRISP-DM): Patterns, data mining tasks, methods (examples)

Global patterns Description

Clustering K-means, EM, hierarchical clustering, ...

Hidden Markov Models Link patterns (e.g., ciation analysis à la Google)

Prediction Classification

Bayes techniques, Decision trees, Support Vector Machines, ...

Regression Time series analysis

Local patterns Frequent itemsets, sequences, subgraphs

» A priori and methods derived from it Association rules Cliques (“Web Communities“)

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Recall: from clustering to ontology learning

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http://www.cs.washington.edu/research/textrunner/

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http://quest.sandbox.yahoo.net

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New material: usageOther examples

E-commerce questions How do people utilize (or not) service

options? Which advertising campaign brings the

most Visitors Customers ? , Google Analytics

E-commerce / information systems questions

What do queries tell us about which content we should inform about?

Search-engine questions How can click-through behaviour aid

relevance assessments re-ranking (“learning to rank“) query recommendation

Personalization (based on explicit or implicit features – e.g. gender prediction)

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Example: Google Analytics Advertising ROI

Visualize the Conversion Funnel

Cross Channel and Multimedia Tracking, Benchmarking

Customized Reporting (define your own metrics)

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Who is this?(Sample from a search-query log)

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Result(a 1-identified person)

[M. Barbaro and T. Zeller. A face is exposed for AOL Searcher No. 4417749. New York Times, 9 August 2006]

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Is this the same person?

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Thanks ! – Questions ?

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Further reading : An excellent textbook introduction

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