Mining the World Wide Web: An Information Search ApproachSpringer Science & Business Media, 2001 M07 31 - 168 pages Mining the World Wide Web: An Information Search Approach explores the concepts and techniques of Web mining, a promising and rapidly growing field of computer science research. Web mining is a multidisciplinary field, drawing on such areas as artificial intelligence, databases, data mining, data warehousing, data visualization, information retrieval, machine learning, markup languages, pattern recognition, statistics, and Web technology. Mining the World Wide Web presents the Web mining material from an information search perspective, focusing on issues relating to the efficiency, feasibility, scalability and usability of searching techniques for Web mining. Mining the World Wide Web is designed for researchers and developers of Web information systems and also serves as an excellent supplemental reference to advanced level courses in data mining, databases and information retrieval. |
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Contents
KEYWORDBASED SEARCH ENGINES | 3 |
1 Search Engines | 4 |
2 Web Directories | 15 |
3 MetaSearch Engines | 16 |
4 Information Filtering | 17 |
QUERYBASED SEARCH SYSTEMS | 19 |
I W3QSW3QL | 20 |
2 WebSQL | 26 |
TEXT MINING | 81 |
2 Association Discovery | 82 |
3 Trend Discovery | 84 |
4 Event Detection | 87 |
WEB MINING | 93 |
2 Web Usage Mining | 95 |
3 Web Structure Mining | 100 |
WEB CRAWLING AGENTS | 105 |
3 WAQL | 29 |
MEDIATORS AND WRAPPERS | 35 |
1 LORE | 38 |
2 ARANEUS | 41 |
3 AKIRA | 47 |
MULTIMEDIA SEARCH ENGINES | 51 |
1 Text or KeywordBased Search | 53 |
2 ContentBased Search | 59 |
DATA MINING | 67 |
2 OnLine Analytical Processing | 68 |
3 Pattern Extraction Processes | 69 |
Other editions - View all
Mining the World Wide Web: An Information Search Approach George Chang,Marcus Healey,James A. M. McHugh,T.L. Wang No preview available - 2012 |
Common terms and phrases
access logs algorithm analysis architecture association rules attribute audio Broker caching Chapter classification clause client cluster color histogram Computer concept classes contain content-based crawlers crawling agent data cube Data Engineering data mining data warehouse Database System DBMS defined denotes described discovered documents domain edge EnviroDaemon environmental example extraction filtering format Fragment Gatherer graph HIST hyperlink hypertext illustrated in Figure index servers information retrieval Information Search Internet inverted file keyword-based keywords Knowledge Discovery matching meta-search methods multimedia search nodes object OLAP partitioning pattern discovery Proceedings processors programs query language query system regular expression relevant result Retrieving Module scheme search engines semantics semistructured similar specify string structure techniques term text mining tree usage vectors Web crawlers Web mining Web server Web usage mining Web-graph Website words World Wide Web
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