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Data Mining Process in Web Applications: Evolution, Applications, and Techniques

This outline delves into the evolution of technology in data mining, its applications in web environments, and the process involved in extracting valuable insights from large datasets. Understand the significance of data warehouses, the concept of knowledge discovery, and the role of data mining in enhancing business intelligence. Explore web mining, social data mining, and techniques to preserve privacy while leveraging data mining technology. Discover how data mining is utilized in various sectors like fraud detection, marketing strategy development, and customer management. Learn about the data mining process from creating a database to deploying and evaluating mining models within the context of data warehousing.

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Data Mining Process in Web Applications: Evolution, Applications, and Techniques

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  1. DATA MINING Process with Reference To Web Application

  2. Outline • Introduction • Data Where house Usage • Evolution of Technology • What is Data Mining ? • Data Mining Application • Data Mining in Use • Data Mining Process • Works with Data Where house

  3. Introduction  Nowadays there is also a great amount of applications and services that are available through Internet as they are seeking, chats, sales, etc.  Knowledge discovery is a concept that describes the process of searching on large volumes of data for patterns that can be considered knowledge about the data.

  4. Data Where house Usage • Three kinds of data warehouse applications • Information processing:- Supports querying, basic statistical analysis, and reporting using cross tabs, tables, charts and graphs. • Analytical processing:- Multidimensional analysis of data warehouse data supports basic OLAP operations, slice-dice, drilling, pivoting. Data mining:- Knowledge discovery from hidden patterns supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization Tools.

  5. Evolution of Technology • 1960s: • Data collection, database creation, IMS and network DBMS. • 1970s: • Relational data model, relational DBMS implementation. • 1980s: • RDBMS, advanced data models (extended-relational database ), • Application-oriented DBMS (spatial, scientific, engineering, etc.). • 1990s: • Data mining, data warehousing and Web databases. • 2000s: • Data mining and its applications Web technology (XML, data • integration) and global information systems.

  6. What is Data Mining..?? Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential.  Data mining is a knowledge discovery process in large and complex data sets, refers to extracting or “mining” knowledge from large amounts of data.

  7. What is Data Mining..??

  8. Web Mining Social data mining Preserving Privacy Data Mining (PPDM) Data Mining Application

  9. Usually, web mining is categorized as web content mining and web usage mining. Both usage and content attributes of a site is described. Some techniques based on clustering and association rules are proposed. Web Mining

  10. Social data mining is a new and challenging aspect of data mining. Social data mining includes communities, searching for multimedia data (images, video, digital photos, audio recordings etc) . It also search personalization search methods for social activities (find friends), text mining for blogs or other forums. Social Data Mining

  11. More recently, stated that “privacy is about self-possession, autonomy, and integrity.” Another view is corporate privacy – the release of information about a collection of data rather than an individual data item. For example:-I may not be concerned about someone knowing my birth date, mother’s name, or social security number. Preserving Privacy Data Mining (PPDM)

  12. The US Government uses Data Mining to track fraud A Supermarket becomes an information broker Basketball teams use it to track game strategy Cross Selling Target Marketing Holding on to Good Customers Weeding out Bad Customers Data Mining in Use

  13. Data Mining Process Creating a database for Data Mining Exploring the Database Preparation for creating a Data Mining Model Building a Data Mining Model Evaluating the Data Mining Model Deploying the Data Mining Model Problem definition

  14. Works with Data Where House • Data Warehousing provides the Enterprise with a memory. • Data Mining provides the Enterprise with intelligence.

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