Data Mining Chris Nelson CS 157 A Fall 2007
Data Mining • New buzzword, old idea. • Inferring new information from already collected data. • Traditionally job of Data Analysts • Computers have changed this. Far more efficient to comb through data using a machine than eyeballing statistical data.
Data Mining – Two Main Components • Wikipedia definition: “Data mining is the entire process of applying computer-based methodology, including new techniques for knowledge discovery, from data.” • Knowledge DiscoveryConcrete information gleaned from known data. Data you may not have known, but which is supported by recorded facts. (ie: Diapers and beer example from previous presentation) • Knowledge PredictionUses known data to forecast future trends, events, etc. (ie: Stock market predictions) • Wikipedia note: "some data mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery.“ These include applications in AI and Symbol analysis
Data Mining vs. Data Analysis • In terms of software and the marketing thereofData Mining != Data Analysis • Data Mining implies software uses some intelligence over simple grouping and partitioning of data to infer new information. • Data Analysis is more in line with standard statistical software (ie: web stats). These usually present information about subsets and relations within the recorded data set (ie: browser/search engine usage, average visit time, etc. )
Data Mining Subtypes • Data DredgingThe process of scanning a data set for relations and then coming up with a hypothesis for existence of those relations. • MetaDataData that describes other data. Can describe an individual element, or a collection of elements. Wikipedia example: “In a library, where the data is the content of the titles stocked, metadata about a title would typically include a description of the content, the author, the publication date and the physical location” • Applications for Data Dredging in business include Market and Risk Analysis, as well as trading strategies. • Applications for Science include disaster prediction.
Propositional vs. Relational Data • Old data mining methods relied on Propositional Data, or data that was related to a single, central element, that could be represented in a vector format. (ie: the purchasing history of a single user. Amazon uses such vectors in its related item suggestions [a multidimensional dot product]) • Current, advanced data mining methods rely on Relational Data, or data that can be stored and modeled easily through use of relational databases. An example of this would be data used to represent interpersonal relations. • Relational Data is more interesting than Propositional data to miners in the sense that an entity, and all the entities to which it is related, factor into the data inference process.
Key Component of Data Mining • Whether Knowledge Discovery or Knowledge Prediction, data mining takes information that was once quite difficult to detect and presents it in an easily understandable format (ie: graphical or statistical) • Data mining Techniques involve sophisticated algorithms, including Decision Tree Classifications, Association detection, and Clustering. • Since Data mining is not on test, I will keep things superficial.
Uses of Data Mining • AI/Machine LearningCombinatorial/Game Data MiningGood for analyzing winning strategies to games, and thus developing intelligent AI opponents. (ie: Chess) • Business StrategiesMarket Basket AnalysisIdentify customer demographics, preferences, and purchasing patterns. • Risk AnalysisProduct Defect AnalysisAnalyze product defect rates for given plants and predict possible complications (read: lawsuits) down the line.
Uses of Data Mining (Continued) • User Behavior ValidationFraud DetectionIn the realm of cell phonesComparing 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.
Uses of Data Mining (Continued) • Health and ScienceProtein FoldingPredicting protein interactions and functionality within biological cells. Applications of this research include determining causes and possible cures for Alzheimers, Parkinson's, and some cancers (caused by protein "misfolds")Extra-Terrestrial IntelligenceScanning Satellite receptions for possible transmissions from other planets. • For more information see Stanford’s Folding@home and SETI@home projects. Both involve participation in a widely distributed computer application.
Sources of Data for Mining • Databases (most obvious) • Text Documents • Computer Simulations • Social Networks
Privacy Concerns • Mining of public and government databases is done, though people have, and continue to raise concerns. • Wiki quote:"data mining gives information that would not be available otherwise. It must be properly interpreted to be useful. When the data collected involves individual people, there are many questions concerning privacy, legality, and ethics."
Data Mining Controversies • Latest one: Facebook's Beacon Advertising program (Just popped on Slashdot within the last week) • What Beacon does: “when you engage in consumer activity at a [Facebook] partner website, such as Amazon, eBay, or the New York Times, not only will Facebook record that activity, but your Facebook connections will also be informed of your purchases or actions.” [taken from http://trickytrickywhiteboy.blogspot.com/2007/11/beware-of-facebooks-beacon.html]
Bottom Line • Data obtained through Data Mining is incredibly valuable • Companies are understandably reluctant to give up data they have obtained. • Expect to see prevalence of Data Mining and (possibly subversive) methods increase in years to come.
Recommended Resources and Works Consulted • Wikipedia Data Mining entryhttp://en.wikipedia.org/wiki/Data_mining • "Privacy is Dead - Get Over It: Revisited" Steve Rambam's Hope Number Six lecturehttp://www.hopenumbersix.net/speakers.html#pid2 • Facebook's Faux Pashttp://www.newsweek.com/id/69275 • Beware of Facebook’s Beaconhttp://trickytrickywhiteboy.blogspot.com/2007/11/beware-of-facebooks-beacon.html • Facebook Data Mining guidehttp://saunderslog.com/2007/11/25/facebook-market-research-secrets/ • Data Mining in Social Networkshttp://kdl.cs.umass.edu/papers/jensen-neville-nas2002.pdf