DATA MINING II - 1DL460. Spring 2014"

Transcription

1 DATA MINING II - 1DL460 Spring 2014" A second course in data mining Kjell Orsborn Uppsala Database Laboratory Department of Information Technology, Uppsala University, Uppsala, Sweden 07/04/14 1

2 Introduction to Data Mining: Web Mining (slides + supplemental articles) ref book (used for slides): Data mining / Dunham" Kjell Orsborn Department of Information Technology Uppsala University, Uppsala, Sweden 07/04/14 2

3 Web Mining Outline" Goal: Examine the use of data mining on the World Wide Web Introduction Web Content Mining Web Structure Mining Web Usage Mining 07/04/14 3

4 Web Mining Issues" Size >350 million pages (1999) Grows at about 1 million pages a day Google indexes 3 billion documents More recent figures: According to a 2001 study, there were more than 550 billion documents (approximately 7,500 terabytes of data) on the Web, mostly in the "invisible web", or deep web. A study, dated January 2005, queried the Google, MSN, Yahoo!, and Ask Jeeves search engines with search terms from 75 different languages and determined that there were over 11.5 billion web pages in the publicly indexable Web, also termed the the surface web. >25 billion pages (2009) in the indexable web (Worldwidewebsize.com) Diverse types of data 07/04/14 4

5 Web data" Web pages Intra-page structures Inter-page structures Usage data Supplemental data Profiles Registration information Cookies 07/04/14 5

6 Web Mining Taxonomy" Modified from [zai01] 07/04/14 6

7 Web content mining" Extends work of basic search engines Search engines IR application Crawlers Indexing Profiles Link analysis Text mining functions (from basic to advanced) Keyword Term associations Similarity search (between query and document) Classification and clustering Natural language processing 07/04/14 7

8 Web Crawlers" Robot (spider) traverses the hypertext structure in the Web. Collect information from visited pages Used to construct indexes for search engines Traditional crawler visits entire Web(?) and replaces index Periodic crawler visits portions of the Web and updates subset of index Incremental crawler selectively searches the Web and incrementally modifies index Focused crawler visits pages related to a particular subject 07/04/14 8

9 Web crawler policies" Web crawler behavior is the result of a combination of policies: a selection policy that states which pages to download, a re-visit policy that states when to check for changes to the pages, a politeness policy that states how to avoid overloading web sites, and a parallelization policy that states how to coordinate distributed web crawlers. 07/04/14 9

10 Web crawler applications" Web search engines Google, Microsoft Bing, (Yahoo), Baidoo, Navier, Yandex, Ask,... One of three base technologies: crawling, indexing and querying (include ranking) I What are the other two and which is the most crucial 07/04/14 10

11 Web archiving Digital preservation Librarian look on the Web The biggest: Internet Archive Batch crawls Primarily collection of national websites Web crawler applications" There are quite many and some are huge! (see the list of Web Archiving Initiatives at Wikipedia) Vertical search engines Data aggregating from many sources on certain topic E.g., apartment search, car search Web data mining To get data to be actually mined Usually using focused crawlers For example, opinion mining Or digests of current happenings on the Web (e.g. what music people listen to now) 07/04/14 11

12 Web crawler applications" Web monitoring Monitoring sites / pages for changes and updates Detection of malicious web sites Typically a part of antivirus, firewall, search engine, etc. service Building a list of such web sites and inform a user about potential threat of visiting such Web site / application testing Crawl a web site to check a navigation through it, validity the links, etc. Regression/security/...testing a rich internet application (RIA) via crawling Checking different application states by simulating possible user interaction events (e.g., mouse click, time-out) Fighting crime! :) well, copyright violations Crawl to find (media) items under copyright or links to them Regular re-visiting suspicious web sites, forums, etc. Tasks like finding terrorist chat rooms also go here 07/04/14 12

13 Web crawler applications" Web scraping Extracting particular pieces of information from a group of typically similar pages When API to data is not available Interestingly, scraping might be more preferable even with API available as scraped data often more clean and up-to-date than data-via-api Web mirroring Copying of web sites Often hosting copies on different servers to ensure constant accessibility 07/04/14 13

14 Example of crawling 07/04/14 14

15 Web crawler architecture" 07/04/14 15

16 Basic sequential crawler" Architecture of sequential crawler: Seeds list of starting URLs The order of page visits determined by frontier data structure Stop condition (e.g. X no of pages fetched) Illustration taken from Ch.8 Web Crawling by Filippo Menczer in Bing Liu s Web Data Mining (Springer, 2007) 07/04/14 16

17 Focused crawler " Focused crawler (when focusing on specific subject also called topical crawler): Only visit links from a page if that page is determined to be relevant. Classifier is static after learning phase. Components: Classifier which assigns relevance score to each page based on crawl topic. Distiller to identify hub pages. Crawler visits pages based on crawler and distiller scores. Classifier relates documents to topics Classifier also determines how useful outgoing links are Hub Pages contain links to many relevant pages. Must be visited even if not high relevance score. 07/04/14 17

18 Focused crawler" 07/04/14 18

19 Context focused crawler" Context Graph: Context graph created for each seed document. Root is the seed document. Nodes at each level show documents with links to documents at next higher level. Updated during crawl itself. Approach: Construct context graph and classifiers using seed documents as training data. Perform crawling using classifiers and context graph created. 07/04/14 19

20 Context graph" 07/04/14 20

21 Virtual web view" Multiple Layered DataBase (MLDB) built on top of the Web. Each layer of the database is more generalized (and smaller) and centralized than the one beneath it. Upper layers of MLDB are structured and can be accessed with SQL type queries. Translation tools convert Web documents to XML. Extraction tools extract desired information to place in first layer of MLDB. Higher levels contain more summarized data obtained through generalizations of the lower levels. 07/04/14 21

22 Personalization" Web access or contents tuned to better fit the desires of each user. E.g. personalized pages, search results and recommendations Include the use of cookies, user accounts, access patterns, databases and more complex data mining techniques Manual techniques perform personalization based on user s registered preferences or classification of individuals based on profiles or demographics. Collaborative filtering identifies preferences based on ratings from similar users. Content based filtering retrieves pages based on similarity between pages and user profiles. 07/04/14 22

23 Web structure mining" Mine structure (links, graph) of the indexable Web Techniques PageRank (Google) HITS (IBM Clever project) Create a model of the Web organization. May be combined with content mining to more effectively retrieve important pages. 07/04/14 23

24 PageRank" Used by Google PageRank was designed to increase the effectiveness of search engines and improve their efficiency Prioritize pages returned from search by looking at Web structure. Importance of page is calculated based on number of pages which point to it i.e. Backlinks. Weighting is used to provide more importance to backlinks coming form important pages. A simplified expression for calculating the PageRank (PR): PR(p) = PR(1)/N PR(n)/N n PR(i): PageRank for a page i which points to target page p. N i : number of links coming out of page i 07/04/14 24

25 HITS - Clever" Developed by IBM Clever project Aims at identifying both authoritative pages and hub pages. Authoritative Pages : Highly important pages. Best source for requested information. Hub Pages : Contain links to highly important pages, i.e. authoritative pages. 07/04/14 25

26 HITS - Clever" Hyperlink-Induced Topic Search (HITS) Based on a given set of keywords (found by a query using a search engine, SE), find a set of relevant pages R (the root set). Identify hub and authority pages for these. Expand R to a base set, B, of pages linked to or from R. Calculate weights for authorities and hubs. Pages with highest ranks in R are returned. 07/04/14 26

27 HITS algorithm" 07/04/14 27

28 Web usage mining" Performs mining on web usage data or web logs (clickstreams) Examined both from a server Uncover info about site where service reside Can e.g. improve design... and a client perspective Uncovers info about user or group Can e.g. improve prefetching and caching Applications of web usage mining Personalization (by e.g. tracking browsing behaviour) Improve structure of a site s web pages Aid in caching and prediction of future page references Improve design of individual pages Improve effectiveness of e-commerce (sales and advertising) 07/04/14 28

29 Web usage mining activities" Preprocessing Web logs (reformatting web log data) Cleanse Remove extraneous information Sessionize, where session is a sequence of pages referenced by one user at a sitting. Pattern discovery Count patterns that occur in sessions Pattern is sequence of pages referenced in a session. Similar to association rules Transaction: session Itemset: pattern (or subset) Order is important Pattern analysis Interpretation of results of pattern discovery 07/04/14 29

30 Web usage mining issues" Identification of exact user not possible. Exact sequence of pages referenced by a user not possible due to caching. Session not well defined Security, privacy, and legal issues 07/04/14 30

31 Web log cleansing" Replace source IP address with unique but non-identifying ID. Replace exact URL of pages referenced with unique but nonidentifying ID. Delete error records and records containing not page data (such as figures and code) 07/04/14 31

32 Sessionizing" Divide Web log into sessions. Two common techniques: Number of consecutive page references from a source IP address occurring within a predefined time interval, such as 30 min (empirical studies show 25,5 min). All consecutive page references from a source IP address where the interclick time is less than a predefined threshold. 07/04/14 32

33 Data structures in web usage mining " Keep track of patterns identified during Web usage mining process Common techniques: Trie Suffix Tree Generalized Suffix Tree WAP Tree 07/04/14 33

34 Trie vs. Suffix tree" Trie: Rooted tree Edges labeled which character (page) from pattern Path from root to leaf represents pattern. Suffix tree: Single child collapsed with parent. Edge contains labels of both prior edges. 07/04/14 34

35 Trie and Suffix tree" 07/04/14 35

36 Generalized Suffix tree" Suffix tree for multiple sessions. Contains patterns from all sessions. Maintains count of frequency of occurrence of a pattern in the node. WAP Tree: Compressed version of generalized suffix tree 07/04/14 36

37 Pattern discovery" Most obvious activity to uncovering traversal patterns, i.e. a set of pages visited by a user in a session Association rules can look at pages accessed together without ordering Similar traversal patterns can be clustered together to provide clustering of users (in contrast to clustering pages to identify similar pages) 07/04/14 37

38 Types of patterns" Algorithms have been developed to discover different types of patterns. Properties: Ordered pages (characters) must occur in the exact order as in the original session. Duplicates duplicate pages are allowed in the pattern. Consecutive all pages in pattern must occur contiguous in a given session. Maximal not subsequence of another pattern. Applications: Prefetching and caching applications: knowledge of contiguous page references frequently made can be useful predicting future references Site/page design: knowledge of frequent backward traversals can be used to improve design Maximal property mainly used to reduce number of meaningful patterns 07/04/14 38

39 Pattern types" Association rules None of the properties hold (no order, no duplicates, no consecutive or maximal patterns) Episodes Only ordering holds Sequential patterns (as applied in web usage mining) Ordered and maximal Forward sequences Backlinks and reloads eliminated Ordered, consecutive, and maximal Maximal frequent sequences Support calculated in reference to length of sequence, i.e. no of clicks All properties hold 07/04/14 39

40 Episodes" Partially ordered set of pages Serial episode totally ordered with time constraint Parallel episode partial ordered with time constraint General episode partial ordered with no time constraint 07/04/14 40

41 DAG for Episode" Temporal ordering of events (page visits) where numbers indicate time step between nodes 07/04/14 41

42 Comparing properties of traversal patterns" 07/04/14 42