Minning WWW

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Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information.
Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified.
Data mining is about technique for finding and describing Structural Patterns in data.
Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information.
Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified.
Data mining is about technique for finding and describing Structural Patterns in data.

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Minning WWW

  1. 1. DATA MINING. Mining WWW. Sonali. Parab.
  2. 2. Overview: Introduction, Definition. Different types of Data Mining. Elements Requirements and kinds. What is Web Mining? Need and Domains of Web Mining. Web Mining techniques. Web mining tools. 2
  3. 3. Introduction Data Mining refers to the process of analysing the data from different perspectives and summarizing it into useful information. ◊ Analyze data. ◊ Categorize data. ◊ Summarize relationship. ◊ Describing structural patterns. 3
  4. 4. Definition: Data Mining Data mining is the process of finding correlation or patterns among fields in large relational databases. Business Data Mining. Scientific Data Mining. Internet Data Mining. 4
  5. 5. Major elements of Data Mining E T L. Store and manage multidimensional database. Provide access to. Analyze the data. Presentation of data. 5
  6. 6. What is Web Mining? Main application for DataMining “Broadly defined as the automated Discovery and analysis of useful information from Documents, services using data mining 6
  7. 7. Need For Web Mining. Information Data World Wide Web is a popular and interactive medium, ideal for publishing information. It is huge, diverse and dynamic and thus raises issue of scalability, multimedia and temporal data respectively 7
  8. 8. Domains of Web Mining: 8
  9. 9. 9
  10. 10. 1. Web Content Mining. An automatic process that extracts patterns from online information, such as the HTML files, images, or Emails, and it already goes beyond only keywords extraction or some simple statistics of words and phrases in documents. Process of information or resource discovery from millions of source across the WWW 10
  11. 11. a. Agent based approaches. Artificial intelligence system that can “act autonomously or semi – autonomously on behalf of a particular user, to discover and organize Web based information.” b. Data approaches. “Integrating and organizing the heterogeneous and semi – structured data on the Web into more structured and high level collections of resources.” 11
  12. 12. 2. Web Structure Mining. describes organizations of content Intra – page structure information includes the arrangement of various tags. Example : HTML or XML tags. <html> tag becomes the root of the tree. 12
  13. 13. 3. Web Usage Mining. Web servers record and accumulate data. Analysing the web access logs. Understand the user behaviour and the Web structure. 13
  14. 14. Web Mining Techniques. i.Clustering / Classification Used to develop profiles of items with similar characteristics. Ability enhances the discovery of relationships Eg : Classification of Web access logs 14
  15. 15. ii. Association Rules. Rules that govern databases of transactions Used to predict the correlation of items. Presence of one set items in a transaction implies. 15
  16. 16. iii. Path Analysis. Generation of graph that “represents relation[s] defined on Web pages.” Physical layout of a Web site. Sitemap. 16
  17. 17. iv. Sequential Patterns. Web access server transaction logs. Discover sequential patterns Example: user visit patterns over a certain period. 17
  18. 18. 18
  19. 19. Web mining as a tool: Promising tool effective search engine Discovers information from mounds. Predicts user visit habits. Designers gets more reliable information. Eg: Web sites with path helps to save time. 19
  20. 20. Current research: As many researchers believe, it was Etzioni who first came up with the term of Web mining in his paper . He brought out a question: is it practical to mine Web data? He also suggested dividing the Web mining to three processes. The paper opened up a new active research field. There are increasing number of researcher working on this field and do some surveys around the data mining on the Web. The Web mining was clearly categorized as Web content mining, Web structure mining and Web usage mining in till 1999. The research works have been well classified since then. There have been some works around content mining, and structure mining, based on the research of Data mining and Information Retrieval, Information Extraction, and Artificial Intelligence. 20
  21. 21. In the usage mining research area, several groups did distinguished work. R. Cooley et al. in University of Minnesota did in-depth research to all the procedure of usage mining. They proposed a mining prototype WebMiner and derived a system WebSIFT to perform the usage mining, which is relatively practical. O. Zaiane et al. [15] proposed the idea of how to implement the OLAP technique on the Web mining. Their works on the multimedia data also provided a valuable solution for content mining. M. Spiliopoulou et al. focused on the applications of the usage mining. His works on the navigation pattern discovery and web site personalization has special meaning for the e-commerce society and the Web marketplace allocation, and will be very helpful for both Web user and administrator. The Web Utilization Miner system is aninnovative sequential mining system. J. Borges et al. has explored some algorithms to mine the user navigation pattern in [2] and his other papers. He proposed a data mining model to achieve an efficient mining, which captures the user navigation behavior pattern by using Ngrammar approach. 21
  22. 22. Mining Tool:Mozenda Mozenda is a Software as a Service (SaaS) company that enables users of all types to easily and affordably extract and manage web data. With Mozenda, users can set up agents that routinely extract data, store data, and publish data to multiple destinations. Once information is in the Mozenda systems users can format, repurpose, and mashup the data to be used in other online/offline applications or as intelligence. All data in the Mozenda system is secure and is hosted in class A data warehouses but can be accessed over the web securely via the Mozenda Web Console. With the addition of a fully featured REST API, Companies can now seamlessly integrate their data automation with the Mozenda application. 22
  23. 23. Conclusion: Data mining is a useful tool with multiple algorithms that can be tuned for specific tasks. It benefits business, medical, and science. 23
  24. 24. Reference: www.datawarehousingonline .com Data base System – Elmasri, Navathe. Data Mining Technologies – Arun K Pujari. http://www.cse.aucegypt.ed u/~rafea/CSCE564/sldes/W ebMiningOverview.pdf http://www.mozenda.com/w eb-mining-software 24
  25. 25. Th u. Yo ank 25

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