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Data Mining: Text and web mining
This document discusses text and web mining. It defines text mining as analyzing huge amounts of text data to extract information. It discusses measures for text retrieval like precision and recall. It also covers text retrieval and indexing methods like inverted indices and signature files. Query processing techniques and ways to reduce dimensionality like latent semantic indexing are explained. The document also discusses challenges in mining the world wide web due to its size and dynamic nature. It defines web usage mining as collecting web access information to analyze paths to accessed web pages.
Data Mining: Text and web mining
- 1. Text and WebMining
- 2. What is TextMining?Text Data Analysis and Information Retrieval Information retrieval (IR) is a field that has been developing in parallel with database systems for many years.Text mining is process of analyzing huge text data to retrieve the information from it.
- 3. Basic Measures forText RetrievalPrecision: This is the percentage of retrieved documents that are in fact relevant tothe query (i.e., “correct” responses). It is formally defined asRecall: This is the percentage of documents that are relevant to the query and were,in fact, retrieved.
- 4. Retrieval and IndexingTextRetrieval Methods 1) Document selection methods2) Document ranking methodsText Indexing Techniques 1) Inverted indices2) Signature files.
- 5. Query Processing TechniquesOncean inverted index is created for a document collection, a retrieval system can answer a keyword query quickly by looking up which documents contain the query keywords.
- 6. Ways of dimensionalityReduction for Text1)Latent Semantic Indexing2) Locality Preserving Indexing3) Probabilistic Latent Semantic IndexingProbabilistic Latent Semantic Indexing schemas :1) Keyword-Based Association Analysis2) Document Classification Analysis3) Document Clustering Analysis
- 7. Mining WWWMiningWorld wide webThe WWW is a huge, widely distributed, global information service center for news, advertisements , management, education, government, and many other information services. The Web also contains a rich and dynamic collection of hyperlink information and Web page access and usage information, providing rich sources for data mining.
- 8. Challenges in miningWWWThe Web seems to be too huge for effective data warehousing and data miningThe complexity of Web pages is far greater than that of any traditional text document collectionThe Web is a highly dynamic information sourceThe Web serves a broad diversity of user communitiesOnly a small portion of the information on the Web is truly relevant or useful
- 9. Web Usage MiningWebusage mining is the third category in web mining. This type of web mining allows for the collection of Web access information for Web pages. This usage data provides the paths leading to accessed Web pages. This information is often gathered automatically into access logs via the Web server.
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