![Warning :: Prevalence of Data Mining
• Your data is already being mined, whether you like it or not.
• Many web services require that you allow access to your information [for
data mining] in order to use the service.
• Google mines email data in Gmail accounts to present account owners
with ads.
• Facebook requires users to allow access to info from non-Facebook pages.
Facebook privacy policy:
"We may use information about you that we collect from other sources,
including but not limited to newspapers and Internet sources such as
blogs, instant messaging services and other users of Facebook, to
supplement your profile.](https://image.slidesharecdn.com/dataminingbyit-180118140241/85/Data-Mining-in-Operating-System-13-320.jpg)
Data Mining in Operating System
- 1. Data Mining Muhammad Farhan Arif 112 Muhammad Arslan Touqeer 117 Ahmad Yaqoob 128 Muhammad Umer Mehboob 131
- 2. Data mining • Data mining is the process of discovering interesting patterns (or knowledge) from large amounts of data. • Data mining is also called knowledge discovery and data mining (KDD) • Wikipedia definition: “Data mining is the entire process of applying computer-based methodology, including new techniques for knowledge discovery, from data.” – Process of semi-automatically analyzing large databases to find patterns that are sources valid, novel, potentially useful, understandable
- 3. –Source: • Databases (most obvious) • Text Documents • Computer Simulations (web) • Social Networks • Image. Data Mining Tasks 1_Classification 2_Regression 3_Deviation detection 4_Clustring 5_ Association Rule Discovery 6_Sequential Pattern Discovery
- 4. • Components/Functionalities: There are two main components: • Knowledge Discovery Concrete information gleaned from known data. Data you may not have known, but which is supported by recorded facts. Also known as descriptive Knowledge Prediction Uses known data to forecast future trends, events, etc. Also known as predictive.(ie: Stock market predictions)
- 5. Classification : In this we do able to identify or predict what class we are talking about. e.G in business we reduce the cost of mailing by electing and sending mail to those consumers which are likely to purchase those products. Regressions: Predict the values of the given continuous variable based on the values of variable , by assuming the given linear or nonlinear values. e.g predicting the predict the financial status of company by data mining of last few years Deviation Detection: Detect the significant deviation from the normal behavior. E.g credit card fraud detection.
- 6. 12 Clustering Given a set of data points , each set has attribute and similarity b/w them. The fin d cluster that , data points in one cluster are more similar to each other and in separate cluster are less similar to one another Association rule : When we are given the set of records and by association , it makes the proper associated items , grouped each other. E.gmemory card and mobile. Sequential Pattered Discovery : Set of objects, associated with its own timeline of events then finding the rules that strongly predict the strong Sequential dependencies . E.g (intro to c++)(c++ premier)---TCl, TCK
- 7. Architecture of Data Mining DATA CLEANING DATA INTRGRATION DATA SELECTION DATA TRANSFORMATION DATA MINING PATTEREN EVALUATION KNOWLEDGE REPRESENTAION
- 8. • Rapid computerization of businesses produce huge amount of data • How to make best use of data? • A growing realization: knowledge discovered from data can be used for competitive advantage. • Make use of your data assets • There is a big gap from stored data to knowledge; and the transition won’t occur automatically. • Many interesting things you want to find cannot be found using database queries “find me people likely to buy my products” “Who are likely to respond to my promotion”
- 9. Uses: • Business Strategies• Market Basket Analysis Identify customer demographics, preferences, and purchasing patterns. • AI/Machine Learning• Combinatorial/Game Data Mining Good for analyzing winning strategies to games, and thus developing intelligent AI opponents. • Risk Analysis• Product Defect Analysis Analyze product defect rates for given plants and predict possible complications • Scientific Analysis:
- 10. • User Behavior Validation Fraud Detection In the realm of cell phones Comparing phone activity to calling records. Can help detect calls made on cloned phones. Similarly, with credit cards, comparing purchases with historical purchases. Can detect activity with stolen cards. • Extra-Terrestrial Intelligence Scanning Satellite receptions for possible transmissions from other planets. Uses
- 11. When/Why we do data mining • The data is abundant. • The data is being warehoused. • The computing power is affordable. • The competitive pressure is strong. • Data mining tools have become available
- 12. + _ camera Display Battery weight durable Mobile 1 vs Mobile 2 M1 M2
- 13. Warning :: Prevalence of Data Mining • Your data is already being mined, whether you like it or not. • Many web services require that you allow access to your information [for data mining] in order to use the service. • Google mines email data in Gmail accounts to present account owners with ads. • Facebook requires users to allow access to info from non-Facebook pages. Facebook privacy policy: "We may use information about you that we collect from other sources, including but not limited to newspapers and Internet sources such as blogs, instant messaging services and other users of Facebook, to supplement your profile.