• Text Mining is the procedure of synthesizing information, by analyzing relations, patterns, and rules among textual data-semi structured or unstructured text.
• This procedure contains text summarization, text categorization and text clustering.
• Text summarization is the procedure to extract its partial content reflection to its whole contents automatically.
• Text categorization is the procedure of assigning a category to the text among categories predefined by users.
• Text clustering is the procedure of segmenting texts into several clusters, depending on the substantial relevance.

Techniques:

• Data mining
• Machine learning
• Information retrieval
• Statistics
• Natural –language understanding
• Case-based reasoning

Text Mining Approaches:

1. Keyword based Association Analysis:

   • Collect sets of keywords or terms that occur frequently together and then find the association or correlation relationship among them.
   • First preprocess the text data by parsing, stemming, removing stop words, etc.
   • Then evoke association mining algorithms -Consider each document as a transaction -View a set of keywords in the document as set of items in the transaction.
   • Term level association mining
   • No need for human effort in tagging documents. -The number of meaningless results and the execution time is greatly reduced.

2. Document Classification Analysis:

   Automatic document classification:

   • Automatic classification for the tremendous number of on-line text documents(Web pages, emails, etc)
   • Text document classification differs from the classification of relational data as document databases are not structured according to attribute-value pairs.

Association-Based Document Classification:

• Extract keywords and terms by information retrieval and simple association analysis techniques.
• Obtain concept hierarchies of keywords and terms using Available term classes such as WordNet, Expert knowledge.
• Classify documents in the training set into class hierarchies.
• Apply term association mining method to discover sets of associated terms.
• Use the term to maximally distinguish one class of documents from others.
• Derive a set of association rules associated with each document class.
• Order the classification rule based on their occurrence frequency and discriminative power.
• Used the rules to classify new documents.

3. Document Clustering Analysis:

• Automatically group related documents based on their contents.
• Require no training sets or predetermined taxonomies, generate a taxonomy at runtime,
• Major steps:
• Preprocessing: Remove stop words, stem, feature extraction.
• Hierarchical clustering: Compute similarities applying clustering algorithms.
• Slicing: Fan out controls; flatten the tree to configurable number of levels.