DATA MINING:

• Data mining (sometimes called data or knowledge discovery) is the computational  process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both.
• The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
• Data mining always depends on effective data collection and warehousing as computer processing.
• Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified, based upon the end user queries Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
• Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought.
• Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
• Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
• Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
• Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.
• The Different levels of analysis are used by data mining softwares include the following:
• Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
• Genetic algorithms: Optimization techniques that use processes 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).
• 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.
• Data mining process consists of five major elements:
• Extract, transform, and load transaction data onto the data warehouse system.
• Store and manage the data in a multidimensional database system.
• Provide data access to business analysts and information technology professionals.
• Analyze the data by application software.
• Present the data in a useful format, such as a graph or table.