9th week

we learned about privacy and public
privacy is the quality or state of being apart from company or observation and seclusion : freedom from unauthorized intrusion(one's right to privacy. and i knew that public and private correlate. rightly, public contains privacy. because, to privacy, we give up public. to public, we give up privacy. for example, the surveillance.
surveilance is that close watch kept over someone or something. surveilance's etymology is that French, from surveiller to watch over, from sur- + veiller to watch, from Latin vigilare, from vigil watchful.
and about surveilance, Benjamin Franklin is said that they that can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety. this is, prove relationship privacy and public. in other word, privacy give up some public.
today's surveillance technology is developed in the past. for example, Viisage & superbowl XXXV.
especially, the company viisage, biometrics base united the preservation of public peace, show us high-level technologies of surveillance. and we learned about surveillance model and capture model.
Surveillance model is built upon visual metaphors and derives from historical experiences of secret police surveillance. Versus, Capture model is built upon linguistic metaphors and takes as its prototype the deliberate reorganization of industrial work activities to allow computers to track them in real time.
privacy and public relationship, Lawrence Lessing is said that an inefficiency that makes it harder for these technologies to be misused. Life where less is monitored is a life more private and life where less can (legally perhaps)be searched is also a life more private.
and we learned about date mining.
goals of data mining is that toward the generation of rules for the classification of objects. and discriminating, or distinguishing between two related, but meaningfully distinct classes.
types of data mining are descriptive and predictive. descriptive is compute a relatively concise, description of a large data set.
predictive is predict unknown values for a variable for one or more known variables.
and data mining tasks are regression, classification, clustering, inference of associative rules and inference of sequential patterns.