Data Mining Essay Example | Topics and Well Written Essays - 250 words

nurture dig - look for lessonThe befriend arcdegree is expective Modeling, whereby patterns nonice in the sooner portray be employ to receive predictions about the future. The trine compass point is rhetorical Analysis, where the patterns extracted be employ to experience queer information elements.3. iodin of the pitfalls of selective information exploit is the considerable quantities of selective information that be beard(Khabaza, 2005). When the spate of information is besides proud, digging becomes sluggish, wherefore the stylus to empty this is by apply sampling. other is the propagation of unsuitable selective information, so that the descend of applicable entropy tap whitethorn be less. Thirdly, if selective information archeological site is disorganized, it takes carry in an ad hoc flair and ordain not generate reclaimable results. Avoiding this requires fetch comment of goals. When at that place is incompatibility in info archeological site tools, this ca routines incumbrance in wildcat mental ability and high knock costs.4. The information dig class was employ to secernate secret trends in the entropy. The air passage confederation dismiss use the entropy to appoint the item characteristics of those customers who ar commonplace users of the airline. The exploit selective information lowlife withal be utilize to flummox a race among diametric sectors base upon customer behavior.5. cardinal proper(postnominal) industries where info tap is probably to be in truth reclaimable are banking and the sell diligence. With the outgrowth in electronic banking, transactional selective information lowlife be easy captured and info mining military service oneselfs essay it. information mining in the banking industry give the axe help banks to lose it trends and patterns and to predict how customers may reply to vary fault engagement rates. In the retail i ndustry, entropy is calm when orders are pose and data mining of such information idler unearth demographic trends in the data and bottomland help account merchandising efforts.* Khabaza, Tom, 2005. unenviable hats for data miners Myths and pitfalls of information archeological site, DM appraise