 Present the data in a useful format, such as a graph or table. 

## 1.3.2 Different Levels Of Analysis Are Available:

 **Artificial neural networks**: Non-linear predictive models that learn through training and resemble biological neural networks in structure.

 **Genetic algorithms**: Optimization techniques that use process such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution. 

 **Decision trees**: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection 
(CHAID). CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART 
segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID. 

 **Nearest neighbor method**: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique. 

 **Rule induction**: The extraction of useful if-then rules from data based on statistical significance. 

 **Data visualization**: The visual interpretation of 

complex relationships in multidimensional data. 

Graphics tools are used to illustrate data relationships

## 1.3.3 Operations Of Web Mining:

 **Data/information extraction**: Our focus will be on extraction of structured data from Web pages, such as products and search results. 

Extracting such data allows one to provide services. Two main types of techniques, machine learning and automatic extraction are covered. 

 **Web information integration and** 
schema matching: Although the Web contains a huge amount of data, each web site (or even page) 
represents similar information differently. How to identify or match semantically similar data is a very important problem with many practical applications. Some existing techniques and problems are examined. 

 **Opinion extraction from online sources**: 
There are many online opinion sources, e.g., 
customer reviews of products, forums, blogs and chat rooms. Mining opinions (especially consumer opinions) is of great importance for marketing intelligence and product benchmarking. We will introduce a few tasks and techniques to mine such sources. 

 **Knowledge synthesis**: Concept hierarchies or ontology are useful in many applications. However, generating them manually is very time consuming. A few existing methods that explores the information redundancy of the Web will be presented. The main application is to synthesize and organize the pieces of information on the Web to give the user a coherent picture of the topic domain.. 

 Segmenting Web pages and detecting noise: In many Web applications, one only wants the main content of the Web page without advertisements, navigation links, copyright notices. 

Automatically segmenting Web page to extract the main content of the pages is interesting problem. A number of interesting techniques have been proposed in the past few years. 