Web mining and text mining involve discovering new knowledge from web content and unstructured text. Web mining includes web content mining of web pages, search results, and web structure mining of page rank algorithms, links between pages, and clustering similar pages. Text mining applies techniques from information retrieval, extraction, and linguistics to extract features from unstructured text like word occurrences and relationships to analyze information.

1. Web Mining and
Text Mining

2. Web Mining
Low precision
Low Recall
Discovering new knowledge from the web
Personalised web page synthesis
Learning about individual users

5. Web Content Mining
Web page Content Mining
Search Result Mining

6. Web Structure Mining
1. Page Rank
i. PageRank Algorithm
ii. Standing of a Node
2. Traversing and Intrinsic Links
3. Reference Nodes and Index Nodes
i. Index nodes
ii. Reference Nodes
4. Clustering and Determining Similar pages
i. Bibliographic Coupling
Bibliographic coupling occurs when two works reference a common third work in their bibliographies.
ii. Co-citation
Co-citation is defined as the frequency with which two documents are cited together by other documents.
[1]
If at least
one other document cites two documents in common these documents are said to be co-cited.

7. Bibliographic Coupling Co-Citation

8. Web Usage Mining
General Access Pattern Tracking
Customized Usage Tracking

9. Text Mining
Information Retrieval
Information Extraction
Computational Linguistics

11. Unstructured Text
● Features
○ Word Occurrences
○ Stop Words
○ Latent Semantic Indexing
○ Stemming
○ n-GRAM
○ POS (Part-of-Speech)
○ Positional Collocations
○ Higher Order Features

12. n-Gram

13. Episode Rule Discovery for Texts
Hierarchy of Categories
Text Clustering
● Scatter/Gather