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. Finally, it discusses challenges in web mining like the huge size and dynamic nature of the web and how web usage mining allows collection of web access information from server logs.

1. Text and Web Mining

2. What is Text Mining? 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 for Text Retrieval Precision: This is the percentage of retrieved documents that are in fact relevant tothe query (i.e., “correct” responses). It is formally defined as Recall: This is the percentage of documents that are relevant to the query and were,in fact, retrieved.

4. Retrieval and Indexing Text Retrieval Methods 1) Document selection methods2) Document ranking methods Text Indexing Techniques 1) Inverted indices2) Signature files.

5. Query Processing Techniques Once an 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 dimensionality Reduction for Text1)Latent Semantic Indexing2) Locality Preserving Indexing3) Probabilistic Latent Semantic Indexing Probabilistic Latent Semantic Indexing schemas : 1) Keyword-Based Association Analysis2) Document Classification Analysis3) Document Clustering Analysis

7. Mining WWW Mining World wide web The 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 mining WWW The Web seems to be too huge for effective data warehousing and data mining The complexity of Web pages is far greater than that of any traditional text document collection The Web is a highly dynamic information source The Web serves a broad diversity of user communities Only a small portion of the information on the Web is truly relevant or useful

9. Web Usage Mining Web usage 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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