Machine Learning and Data Mining: 19 Mining Text And Web Data

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Information about Machine Learning and Data Mining: 19 Mining Text And Web Data

Published on June 3, 2007

Author: pierluca.lanzi



Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we overview text and web mining. The slides are mainly taken from Jiawei Han textbook.

Text and Web Mining Machine Learning and Data Mining (Unit 19) Prof. Pier Luca Lanzi

References 2 Jiawei Han and Micheline Kamber, quot;Data Mining: Concepts and Techniquesquot;, The Morgan Kaufmann Series in Data Management Systems (Second Edition) Chapter 10, part 2 Web Mining Course by Gregory-Platesky Shapiro available at Prof. Pier Luca Lanzi

Mining Text Data: An Introduction 3 Data Mining / Knowledge Discovery Structured Data Multimedia Free Text Hypertext <a href>Frank Rizzo Frank Rizzo bought HomeLoan ( </a> Bought his home from Lake Loanee: Frank Rizzo <a hef>this home</a> View Real Estate in Lender: MWF from <a href>Lake 1992. Agency: Lake View View Real Estate</a> He paid $200,000 Amount: $200,000 In <b>1992</b>. under a15-year loan Term: 15 years Loans($200K,[map],...) <p>... from MW Financial. ) Prof. Pier Luca Lanzi

Bag-of-Tokens Approaches 4 Documents Token Sets Four score and seven nation – 5 years ago our fathers brought civil - 1 forth on this continent, a new war – 2 nation, conceived in Liberty, Feature men – 2 and dedicated to the Extraction died – 4 proposition that all men are people – 5 created equal. Liberty – 1 Now we are engaged in a God – 1 great civil war, testing … whether that nation, or … Loses all order-specific information! Severely limits context! Prof. Pier Luca Lanzi

Natural Language Processing 5 A dog is chasing a boy on the playground Lexical analysis Det Noun Aux Verb Det Noun Prep Det Noun (part-of-speech tagging) Noun Phrase Noun Phrase Noun Phrase Complex Verb Prep Phrase Semantic analysis Verb Phrase Syntactic analysis Dog(d1). (Parsing) Boy(b1). Verb Phrase Playground(p1). Chasing(d1,b1,p1). Sentence + Scared(x) if Chasing(_,x,_). A person saying this may be reminding another person to get the dog back… Scared(b1) Pragmatic analysis Inference (speech act) (Taken from ChengXiang Zhai, CS 397cxz – Fall 2003) Prof. Pier Luca Lanzi

General NLP—Too Difficult! 6 Word-level ambiguity “design” can be a noun or a verb (Ambiguous POS) “root” has multiple meanings (Ambiguous sense) Syntactic ambiguity “natural language processing” (Modification) “A man saw a boy with a telescope.” (PP Attachment) Anaphora resolution “John persuaded Bill to buy a TV for himself.” (himself = John or Bill?) Presupposition “He has quit smoking.” implies that he smoked before. Humans rely on context to interpret (when possible). This context may extend beyond a given document! (Taken from ChengXiang Zhai, CS 397cxz – Fall 2003) Prof. Pier Luca Lanzi

Shallow Linguistics 7 English Lexicon Part-of-Speech Tagging Word Sense Disambiguation Phrase Detection / Parsing Prof. Pier Luca Lanzi

WordNet 8 An extensive lexical network for the English language Contains over 138,838 words. Several graphs, one for each part-of-speech. Synsets (synonym sets), each defining a semantic sense. Relationship information (antonym, hyponym, meronym …) Downloadable for free (UNIX, Windows) Expanding to other languages (Global WordNet Association) Funded >$3 million, mainly government (translation interest) Founder George Miller, National Medal of Science, 1991. watery parched moist dry arid wet synonym antonym damp anhydrous Prof. Pier Luca Lanzi

Part-of-Speech Tagging 9 Training data (Annotated text) This sentence serves as an example of annotated text… Det N V1 P Det N P V2 N This is a new sentence. POS Tagger “This is a new sentence.” Det Aux Det Adj N Pick the most1likely, ttag tsequence. p(w ,..., wk 1,..., k ) ⎧ p(t1 | w1 )... p(tk | wk ) p(w1 )... p(wk ) ⎪ p(w1 ,..., wk , t1 ,..., tk ) = ⎨ k ⎪∏ p(wi | ti ) p(ti | ti−1 ) Independent assignment ⎩ =1 ⎧ p(t1 | w1 )... p(tk | wk ) p(iw1 )... p(wk ) Most common tag ⎪ =⎨ k ⎪∏ p(wi | ti ) p(ti | ti −1 ) ⎩ i =1 Partial dependency (HMM) Prof. Pier Luca Lanzi

Word Sense Disambiguation 10 ? “The difficulties of computational linguistics are rooted in ambiguity.” N Aux V P N Supervised Learning Features: Neighboring POS tags (N Aux V P N) Neighboring words (linguistics are rooted in ambiguity) Stemmed form (root) Dictionary/Thesaurus entries of neighboring words High co-occurrence words (plant, tree, origin,…) Other senses of word within discourse Algorithms: Rule-based Learning (e.g. IG guided) Statistical Learning (i.e. Naïve Bayes) Unsupervised Learning (i.e. Nearest Neighbor) (Adapted from ChengXiang Zhai, CS 397cxz – Fall 2003) Prof. Pier Luca Lanzi

Parsing 11 (Adapted from ChengXiang Zhai, CS 397cxz – Fall 2003) Choose most likely parse tree… S Probability of this tree=0.000015 NP VP Probabilistic CFG Det S→ NP VP 1.0 BNP VP PP NP → Det BNP 0.3 NP → BNP A 0.4 N Aux V NP P NP 0.3 NP→ NP PP Grammar is chasing on BNP→ N dog … VP → V a boy the playground . VP → Aux V NP . . … VP → VP PP Probability of this tree=0.000011 S PP → P NP 1.0 NP VP V → chasing 0.01 Det NP BNP Aux V Aux→ is PP N → dog 0.003 A chasing NP N is N → boy Lexicon NP P N→ playground … dog a boy Det→ the on … Det→ a P → on the playground Prof. Pier Luca Lanzi

Obstacles 12 Ambiguity A man saw a boy with a telescope. Computational Intensity Imposes a context horizon. Text Mining NLP Approach Locate promising fragments using fast IR methods (bag-of-tokens) Only apply slow NLP techniques to promising fragments Prof. Pier Luca Lanzi

Summary: Shallow NLP 13 However, shallow NLP techniques are feasible and useful: Lexicon – machine understandable linguistic knowledge possible senses, definitions, synonyms, antonyms, typeof, etc. POS Tagging – limit ambiguity (word/POS), entity extraction WSD – stem/synonym/hyponym matches (doc and query) Query: “Foreign cars” Document: “I’m selling a 1976 Jaguar…” Parsing – logical view of information (inference?, translation?) A man saw a boy with a telescope.” Even without complete NLP, any additional knowledge extracted from text data can only be beneficial. Ingenuity will determine the applications. Prof. Pier Luca Lanzi

References for NLP 14 C. D. Manning and H. Schutze, “Foundations of Natural Language Processing”, MIT Press, 1999. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, 1995. S. Chakrabarti, “Mining the Web: Statistical Analysis of Hypertext and Semi-Structured Data”, Morgan Kaufmann, 2002. G. Miller, R. Beckwith, C. FellBaum, D. Gross, K. Miller, and R. Tengi. Five papers on WordNet. Princeton University, August 1993. C. Zhai, Introduction to NLP, Lecture Notes for CS 397cxz, UIUC, Fall 2003. M. Hearst, Untangling Text Data Mining, ACL’99, invited paper. tdm.html R. Sproat, Introduction to Computational Linguistics, LING 306, UIUC, Fall 2003. A Road Map to Text Mining and Web Mining, University of Texas resource page. mining/ Computational Linguistics and Text Mining Group, IBM Research, Prof. Pier Luca Lanzi

Text Databases and Information Retrieval 15 Text databases (document databases) Large collections of documents from various sources: news articles, research papers, books, digital libraries, E-mail messages, and Web pages, library database, etc. Data stored is usually semi-structured Traditional information retrieval techniques become inadequate for the increasingly vast amounts of text data Information retrieval A field developed in parallel with database systems Information is organized into (a large number of) documents Information retrieval problem: locating relevant documents based on user input, such as keywords or example documents Prof. Pier Luca Lanzi

Information Retrieval (IR) 16 Typical Information Retrieval systems Online library catalogs Online document management systems Information retrieval vs. database systems Some DB problems are not present in IR, e.g., update, transaction management, complex objects Some IR problems are not addressed well in DBMS, e.g., unstructured documents, approximate search using keywords and relevance Prof. Pier Luca Lanzi

Basic Measures for Text Retrieval 17 Relevant Relevant & Retrieved Retrieved All Documents Precision: the percentage of retrieved documents that are in fact relevant to the query (i.e., “correct” responses) | {Relevant} ∩ {Retrieved } | precision = | {Retrieved} | Recall: the percentage of documents that are relevant to the query and were, in fact, retrieved | {Relevant} ∩ {Retrieved } | precision = | {Relevant} | Prof. Pier Luca Lanzi

Information Retrieval Techniques 18 Basic Concepts A document can be described by a set of representative keywords called index terms. Different index terms have varying relevance when used to describe document contents. This effect is captured through the assignment of numerical weights to each index term of a document. (e.g.: frequency, tf-idf) DBMS Analogy Index Terms Attributes Weights Attribute Values Prof. Pier Luca Lanzi

Information Retrieval Techniques 19 Index Terms (Attribute) Selection: Stop list Word stem Index terms weighting methods Terms Documents Frequency Matrices Information Retrieval Models: Boolean Model Vector Model Probabilistic Model Prof. Pier Luca Lanzi

Boolean Model 20 Consider that index terms are either present or absent in a document As a result, the index term weights are assumed to be all binaries A query is composed of index terms linked by three connectives: not, and, and or E.g.: car and repair, plane or airplane The Boolean model predicts that each document is either relevant or non-relevant based on the match of a document to the query Prof. Pier Luca Lanzi

Keyword-Based Retrieval 21 A document is represented by a string, which can be identified by a set of keywords Queries may use expressions of keywords E.g., car and repair shop, tea or coffee, DBMS but not Oracle Queries and retrieval should consider synonyms, e.g., repair and maintenance Major difficulties of the model Synonymy: A keyword T does not appear anywhere in the document, even though the document is closely related to T, e.g., data mining Polysemy: The same keyword may mean different things in different contexts, e.g., mining Prof. Pier Luca Lanzi

Similarity-Based Retrieval in Text Data 22 Finds similar documents based on a set of common keywords Answer should be based on the degree of relevance based on the nearness of the keywords, relative frequency of the keywords, etc. Basic techniques Stop list Set of words that are deemed “irrelevant”, even though they may appear frequently E.g., a, the, of, for, to, with, etc. Stop lists may vary when document set varies Prof. Pier Luca Lanzi

Similarity-Based Retrieval in Text Data 23 Word stem Several words are small syntactic variants of each other since they share a common word stem E.g., drug, drugs, drugged A term frequency table Each entry frequent_table(i, j) = # of occurrences of the word ti in document di Usually, the ratio instead of the absolute number of occurrences is used Similarity metrics: measure the closeness of a document to a query (a set of keywords) Relative term occurrences Cosine distance: v1 ⋅ v2 sim(v1 , v2 ) = | v1 || v2 | Prof. Pier Luca Lanzi

Indexing Techniques 24 Inverted index Maintains two hash- or B+-tree indexed tables: • document_table: a set of document records <doc_id, postings_list> • term_table: a set of term records, <term, postings_list> Answer query: Find all docs associated with one or a set of terms + easy to implement – do not handle well synonymy and polysemy, and posting lists could be too long (storage could be very large) Signature file Associate a signature with each document A signature is a representation of an ordered list of terms that describe the document Order is obtained by frequency analysis, stemming and stop lists Prof. Pier Luca Lanzi

Vector Space Model 25 Documents and user queries are represented as m-dimensional vectors, where m is the total number of index terms in the document collection. The degree of similarity of the document d with regard to the query q is calculated as the correlation between the vectors that represent them, using measures such as the Euclidian distance or the cosine of the angle between these two vectors. Prof. Pier Luca Lanzi

Latent Semantic Indexing (1) 26 Basic idea Similar documents have similar word frequencies Difficulty: the size of the term frequency matrix is very large Use a singular value decomposition (SVD) techniques to reduce the size of frequency table Retain the K most significant rows of the frequency table Method Create a term x document weighted frequency matrix A SVD construction: A = U * S * V’ Define K and obtain Uk ,, Sk , and Vk. Create query vector q’ . Project q’ into the term-document space: Dq = q’ * Uk * Sk-1 Calculate similarities: cos α = Dq . D / ||Dq|| * ||D|| Prof. Pier Luca Lanzi

Latent Semantic Indexing (2) 27 Weighted Frequency Matrix Query Terms: - Insulation - Joint Prof. Pier Luca Lanzi

Probabilistic Model 28 Basic assumption: Given a user query, there is a set of documents which contains exactly the relevant documents and no other (ideal answer set) Querying process as a process of specifying the properties of an ideal answer set. Since these properties are not known at query time, an initial guess is made This initial guess allows the generation of a preliminary probabilistic description of the ideal answer set which is used to retrieve the first set of documents An interaction with the user is then initiated with the purpose of improving the probabilistic description of the answer set Prof. Pier Luca Lanzi

Types of Text Data Mining 29 Keyword-based association analysis Automatic document classification Similarity detection Cluster documents by a common author Cluster documents containing information from a common source Link analysis: unusual correlation between entities Sequence analysis: predicting a recurring event Anomaly detection: find information that violates usual patterns Hypertext analysis Patterns in anchors/links Anchor text correlations with linked objects Prof. Pier Luca Lanzi

Keyword-Based Association Analysis 30 Motivation Collect sets of keywords or terms that occur frequently together and then find the association or correlation relationships among them Association Analysis Process Preprocess the text data by parsing, stemming, removing stop words, etc. Evoke association mining algorithms • Consider each document as a transaction • View a set of keywords in the document as a set of items in the transaction Term level association mining • No need for human effort in tagging documents • The number of meaningless results and the execution time is greatly reduced Prof. Pier Luca Lanzi

Text Classification (1) 31 Motivation Automatic classification for the large number of on-line text documents (Web pages, e-mails, corporate intranets, etc.) Classification Process Data preprocessing Definition of training set and test sets Creation of the classification model using the selected classification algorithm Classification model validation Classification of new/unknown text documents Text document classification differs from the classification of relational data Document databases are not structured according to attribute-value pairs Prof. Pier Luca Lanzi

Text Classification (2) 32 Classification Algorithms: Support Vector Machines K-Nearest Neighbors Naïve Bayes Neural Networks Decision Trees Association rule-based Boosting Prof. Pier Luca Lanzi

Document Clustering 33 Motivation Automatically group related documents based on their contents No predetermined training sets or taxonomies Generate a taxonomy at runtime Clustering Process Data preprocessing: remove stop words, stem, feature extraction, lexical analysis, etc. Hierarchical clustering: compute similarities applying clustering algorithms. Model-Based clustering (Neural Network Approach): clusters are represented by “exemplars”. (e.g.: SOM) Prof. Pier Luca Lanzi

Text Categorization 34 Pre-given categories and labeled document examples (Categories may form hierarchy) Classify new documents A standard classification (supervised learning ) problem Sports Categorization Business System Education … … Science Sports Business Education Prof. Pier Luca Lanzi

Applications 35 News article classification Automatic email filtering Webpage classification Word sense disambiguation …… Prof. Pier Luca Lanzi

Categorization Methods 36 Manual: Typically rule-based Does not scale up (labor-intensive, rule inconsistency) May be appropriate for special data on a particular domain Automatic: Typically exploiting machine learning techniques Vector space model based • Prototype-based (Rocchio) • K-nearest neighbor (KNN) • Decision-tree (learn rules) • Neural Networks (learn non-linear classifier) • Support Vector Machines (SVM) Probabilistic or generative model based • Naïve Bayes classifier Prof. Pier Luca Lanzi

Vector Space Model 37 Represent a doc by a term vector Term: basic concept, e.g., word or phrase Each term defines one dimension N terms define a N-dimensional space Element of vector corresponds to term weight E.g., d = (x1,…,xN), xi is “importance” of term I New document is assigned to the most likely category based on vector similarity Prof. Pier Luca Lanzi

Illustration of the Vector Space Model 38 Starbucks C2 Category 2 Category 3 C3 Java new doc C1 Category 1 Microsoft Prof. Pier Luca Lanzi

What VS Model Does Not Specify? 39 How to select terms to capture “basic concepts” Word stopping • e.g. “a”, “the”, “always”, “along” Word stemming • e.g. “computer”, “computing”, “computerize” => “compute” Latent semantic indexing How to assign weights Not all words are equally important: Some are more indicative than others • e.g. “algebra” vs. “science” How to measure the similarity Prof. Pier Luca Lanzi

How to Assign Weights 40 Two-fold heuristics based on frequency TF (Term frequency) More frequent within a document more relevant to semantics E.g., “query” vs. “commercial” IDF (Inverse document frequency) Less frequent among documents more discriminative E.g. “algebra” vs. “science” Prof. Pier Luca Lanzi

Term Frequency Weighting 41 Weighting The more frequent, the more relevant to topic E.g. “query” vs. “commercial” Raw TF= f(t,d): how many times term t appears in doc d Normalization When document length varies, then relative frequency is preferred E.g., Maximum frequency normalization Prof. Pier Luca Lanzi

Inverse Document Frequency Weighting 42 Idea: The less frequent terms among documents are the more discriminative Formula: Given n, total number of docs k, the number of docs with term t appearing (the DF document frequency) Prof. Pier Luca Lanzi

TF-IDF Weighting 43 TF-IDF weighting: weight(t, d) = TF(t, d) * IDF(t) Frequent within doc high tf high weight Selective among docs high idf high weight Recall VS model Each selected term represents one dimension Each doc is represented by a feature vector Its t-term coordinate of document d is the TF-IDF weight This is more reasonable Just for illustration … Many complex and more effective weighting variants exist in practice Prof. Pier Luca Lanzi

How to Measure Similarity? 44 Given two document Similarity definition dot product normalized dot product (or cosine) Prof. Pier Luca Lanzi

Illustrative Example 45 text doc1 mining Sim(newdoc,doc1)=4.8*2.4+4.5*4.5 search engine Sim(newdoc,doc2)=2.4*2.4 text To whom is newdoc more similar? travel text Sim(newdoc,doc3)=0 map doc2 travel text mining travel map search engine govern president congress IDF(faked) 2.4 4.5 2.8 3.3 2.1 5.4 2.2 3.2 4.3 government doc1 2(4.8) 1(4.5) 1(2.1) 1(5.4) president doc2 1(2.4 ) 2 (5.6) 1(3.3) congress doc3 doc3 1 (2.2) 1(3.2) 1(4.3) newdoc 1(2.4) 1(4.5) …… Prof. Pier Luca Lanzi

Vector Space Model-Based Classifiers 46 What do we have so far? A feature space with similarity measure This is a classic supervised learning problem Search for an approximation to classification hyper plane VS model based classifiers K-NN Decision tree based Neural networks Support vector machine Prof. Pier Luca Lanzi

Probabilistic Model 47 Category C is modeled as a probability distribution of predefined random events Random events model the process of generating documents Therefore, how likely a document d belongs to category C is measured through the probability for category C to generate d. Prof. Pier Luca Lanzi

Quick Revisit of Bayes’ Rule 48 Category Hypothesis space: H = {C1 , …, Cn} One document: D P ( D | Ci ) P(Ci ) P (Ci | D) = P( D) As we want to pick the most likely category C*, we can drop p(D) Posterior probability of Ci C* = arg max C P(C | D) = arg max C P ( D | C ) P(C ) Document model for category C Prof. Pier Luca Lanzi

Probabilistic Model: Multi-Bernoulli 49 Event: word presence or absence D = (x1, …, x|V|) xi =1 for presence of word wi xi =0 for absence Parameters {p(wi=1|C), p(wi=0|C)} p(wi=1|C)+ p(wi=0|C)=1 |V | |V | |V | p( D = ( x1 ,..., x|V | ) | C )= ∏ p( wi = xi | C ) = ∏ ∏ p(wi = 1| C ) p( wi = 0 | C ) i =1 i =1, xi =1 i =1, xi =0 Prof. Pier Luca Lanzi

Probabilistic Model: Multinomial 50 Event: word selection/sampling D = (n1, …, n|V|) ni: frequency of word wi n=n1,+…+ n|V| Parameters {p(wi|C)} p(w1|C)+… p(w|v||C) = 1 ⎛ ⎞ |V | n n1 ... n|V | ⎠ ∏ p ( D = ( n1 , ..., n|v | ) | C )= p ( n | C ) ⎜ p ( w i | C ) ni ⎟ ⎝ i =1 Prof. Pier Luca Lanzi

Parameter Estimation 51 Category prior Training examples: | E (Ci ) | p (Ci ) = E(C2) E(C1) k ∑ | E (C ) | j j =1 C1 C2 Multi-Bernoulli Doc model Ck ∑ δ(w ,d)+0.5 ⎧1 if wj occursin d j d∈E(Ci ) δ(wj ,d) = ⎨ p(wj =1| Ci ) = | E(Ci )| +1 ⎩0 otherwise E(Ck) Multinomial doc model Vocabulary: V = {w1, …, w|V|} ∑ c(w ,d)+1 j d∈E(Ci ) p(wj | Ci ) = |V| c(wj ,d) =counts of wj in d ∑ ∑ c(w ,d)+|V | m m=1 d∈E(Ci ) Prof. Pier Luca Lanzi

Classification of New Document 52 Multi-Bernoulli Multinomial d = (x1,..., x|V| ) x ∈{0,1 d = (n1,..., n|V| ) | d |= n = n1 +... + n|V| } C* = argmaxC P(D | C)P(C) C* = argmaxC P(D | C)P(C) |V| = argmaxC p(n | C)∏p(wi | C)ni P(C) |V| = argmaxC ∏p(wi = xi | C)P(C) i=1 i=1 |V| = argmaxC log p(n | C) + log p(C) + ∑ni log p(wi | C) |V| = argmaxC log p(C) + ∑log p(wi = xi | C) i=1 |V| ≈ argmaxC log p(C) + ∑ni log p(wi | C) i=1 i=1 Prof. Pier Luca Lanzi

Categorization Methods 53 Vector space model K-NN Decision tree Neural network Support vector machine Probabilistic model Naïve Bayes classifier Many, many others and variants exist e.g. Bim, Nb, Ind, Swap-1, LLSF, Widrow-Hoff, Rocchio, Gis-W, … … Prof. Pier Luca Lanzi

Evaluation (1) 54 Effectiveness measure Classic: Precision & Recall • Precision • Recall Prof. Pier Luca Lanzi

Evaluation (2) 55 Benchmarks Classic: Reuters collection • A set of newswire stories classified under categories related to economics. Effectiveness Difficulties of strict comparison • different parameter setting • different “split” (or selection) between training and testing • various optimizations … … However widely recognizable • Best: Boosting-based committee classifier & SVM • Worst: Naïve Bayes classifier Need to consider other factors, especially efficiency Prof. Pier Luca Lanzi

Summary: Text Categorization 56 Wide application domain Comparable effectiveness to professionals Manual Text Classification is not 100% and unlikely to improve substantially Automatic Text Classification is growing at a steady pace Prospects and extensions Very noisy text, such as text from O.C.R. Speech transcripts Prof. Pier Luca Lanzi

Research Problems in Text Mining 57 Google: what is the next step? How to find the pages that match approximately the sophisticated documents, with incorporation of user-profiles or preferences? Look back of Google: inverted indicies Construction of indicies for the sophisticated documents, with incorporation of user-profiles or preferences Similarity search of such pages using such indicies Prof. Pier Luca Lanzi

References for Text Mining 58 Fabrizio Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, Vol. 34, No.1, March 2002 Soumen Chakrabarti, “Data mining for hypertext: A tutorial survey”, ACM SIGKDD Explorations, 2000. Cleverdon, “Optimizing convenient online accesss to bibliographic databases”, Information Survey, Use4, 1, 37- 47, 1984 Yiming Yang, “An evaluation of statistical approaches to text categorization”, Journal of Information Retrieval, 1:67-88, 1999. Yiming Yang and Xin Liu “A re-examination of text categorization methods”. Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99, pp 42--49), 1999. Prof. Pier Luca Lanzi

World Wide Web: a brief history 59 Who invented the wheel is unknown Who invented the World-Wide Web ? (Sir) Tim Berners-Lee in 1989, while working at CERN, invented the World Wide Web, including URL scheme, HTML, and in 1990 wrote the first server and the first browser Mosaic browser developed by Marc Andreessen and Eric Bina at NCSA (National Center for Supercomputing Applications) in 1993; helped rapid web spread Mosaic was basis for Netscape … Prof. Pier Luca Lanzi

What is Web Mining? 60 Discovering interesting and useful information from Web content and usage Examples Web search, e.g. Google, Yahoo, MSN, Ask, … Specialized search: e.g. Froogle (comparison shopping), job ads (Flipdog) eCommerce Recommendations (Netflix, Amazon, etc.) Improving conversion rate: next best product to offer Advertising, e.g. Google Adsense Fraud detection: click fraud detection, … Improving Web site design and performance Prof. Pier Luca Lanzi

How does it differ from “classical” 61 Data Mining? The web is not a relation Textual information and 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 warehouses Ability to react in real-time to usage patterns No human in the loop Reproduced from Ullman & Rajaraman with permission Prof. Pier Luca Lanzi

How big is the Web? 62 The number of pages is technically, infinite Because of dynamically generated content Lots of duplication (30-40%) Best estimate of “unique” static HTML pages comes from search engine claims Google = 8 billion Yahoo = 20 billion Lots of marketing hype Reproduced from Ullman & Rajaraman with permission Prof. Pier Luca Lanzi

Netcraft Survey: 63 76,184,000 web sites (Feb 2006) Netcraft survey Prof. Pier Luca Lanzi

Mining the World-Wide Web 64 The WWW is huge, widely distributed, global information service center 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 for data mining Challenges Too huge for effective data warehousing and data mining Too complex and heterogeneous: no standards and structure Prof. Pier Luca Lanzi

Web Mining: A more challenging task 65 Searches for Web access patterns Web structures Regularity and dynamics of Web contents Problems The “abundance” problem Limited coverage of the Web: hidden Web sources, majority of data in DBMS Limited query interface based on keyword-oriented search Limited customization to individual users Prof. Pier Luca Lanzi

Web Mining Taxonomy 66 Web Mining Web Content Web Structure Web Usage Mining Mining Mining Web Page General Access Customized Search Result Content Mining Pattern Tracking Usage Tracking Mining Prof. Pier Luca Lanzi

Mining the World-Wide Web 67 Web Mining Web Content Mining Web Structure Web Usage Mining Mining Web Page Content Mining Web Page Summarization General Access Customized Search Result WebLog , WebOQL …: Pattern Tracking Usage Tracking Mining Web Structuring query languages; Can identify information within given web pages Ahoy!:Uses heuristics to distinguish personal home pages from other web pages ShopBot: Looks for product prices within web pages Prof. Pier Luca Lanzi

Mining the World-Wide Web 68 Web Mining Web Content Web Structure Mining Web Usage Mining Mining Web Page Content Mining Search Result Mining General Access Customized Pattern Tracking Usage Tracking Search Engine Result Summarization Clustering Search Result : Categorizes documents using phrases in titles and snippets Prof. Pier Luca Lanzi

Mining the World-Wide Web 69 Web Mining Web Content Web Usage Mining Mining Web Structure Mining Using Links PageRank CLEVER Use interconnections between web pages to give General Access Search Result weight to pages. Pattern Tracking Mining Using Generalization Web Page Customized MLDB, VWV Content Mining Usage Tracking Uses a multi-level database representation of the Web. Counters (popularity) and link lists are used for capturing structure. Prof. Pier Luca Lanzi

Mining the World-Wide Web 70 Web Mining Web Content Web Structure Web Usage Mining Mining Mining General Access Pattern Tracking Web Page Customized Content Mining Usage Tracking Web Log Mining Search Result Uses KDD techniques to understand Mining general access patterns and trends. Can shed light on better structure and grouping of resource providers. Prof. Pier Luca Lanzi

Mining the World-Wide Web 71 Web Mining Web Content Web Structure Web Usage Mining Mining Mining Customized Usage Tracking Web Page General Access Content Mining Pattern Tracking Adaptive Sites Analyzes access patterns of each user at a Search Result time. Web site restructures itself automatically Mining by learning from user access patterns. Prof. Pier Luca Lanzi

Web Usage Mining 72 Mining Web log records to discover user access patterns of Web pages Applications Target potential customers for electronic commerce Enhance the quality and delivery of Internet information services to the end user Improve Web server system performance Identify potential prime advertisement locations Web logs provide rich information about Web dynamics Typical Web log entry includes the URL requested, the IP address from which the request originated, and a timestamp Prof. Pier Luca Lanzi

Web Usage Mining 73 Understanding is a pre-requisite to improvement 1 Google, but 70,000,000+ web sites What Applications? Simple and Basic • Monitor performance, bandwidth usage • Catch errors (404 errors- pages not found) • Improve web site design (shortcuts for frequent paths, remove links not used, etc) •… Advanced and Business Critical • eCommerce: improve conversion, sales, profit • Fraud detection: click stream fraud, … •… Prof. Pier Luca Lanzi

Techniques for Web usage mining 74 Construct multidimensional view on the Weblog database Perform multidimensional OLAP analysis to find the top N users, top N accessed Web pages, most frequently accessed time periods, etc. Perform data mining on Weblog records Find association patterns, sequential patterns, and trends of Web accessing May need additional information,e.g., user browsing sequences of the Web pages in the Web server buffer Conduct studies to Analyze system performance, improve system design by Web caching, Web page prefetching, and Web page swapping Prof. Pier Luca Lanzi

References 75 Deng Cai, Shipeng Yu, Ji-Rong Wen and Wei-Ying Ma, “Extracting Content Structure for Web Pages based on Visual Representation”, The Fifth Asia Pacific Web Conference, 2003. Deng Cai, Shipeng Yu, Ji-Rong Wen and Wei-Ying Ma, “VIPS: a Vision-based Page Segmentation Algorithm”, Microsoft Technical Report (MSR-TR-2003-79), 2003. Shipeng Yu, Deng Cai, Ji-Rong Wen and Wei-Ying Ma, “Improving Pseudo-Relevance Feedback in Web Information Retrieval Using Web Page Segmentation”, 12th International World Wide Web Conference (WWW2003), May 2003. Ruihua Song, Haifeng Liu, Ji-Rong Wen and Wei-Ying Ma, “Learning Block Importance Models for Web Pages”, 13th International World Wide Web Conference (WWW2004), May 2004. Deng Cai, Shipeng Yu, Ji-Rong Wen and Wei-Ying Ma, “Block-based Web Search”, SIGIR 2004, July 2004 . Deng Cai, Xiaofei He, Ji-Rong Wen and Wei-Ying Ma, “Block-Level Link Analysis”, SIGIR 2004, July 2004 . Deng Cai, Xiaofei He, Wei-Ying Ma, Ji-Rong Wen and Hong-Jiang Zhang, “Organizing WWW Images Based on The Analysis of Page Layout and Web Link Structure”, The IEEE International Conference on Multimedia and EXPO (ICME'2004) , June 2004 Deng Cai, Xiaofei He, Zhiwei Li, Wei-Ying Ma and Ji-Rong Wen, “Hierarchical Clustering of WWW Image Search Results Using Visual, Textual and Link Analysis”,12th ACM International Conference on Multimedia, Oct. 2004 . Prof. Pier Luca Lanzi

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