August 16, 2022 Data Mining: Concepts and Techniques 1 Why Data Preprocessing? Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data e.g., occupation=“ ” noisy: containing errors or outliers e.g., Salary=“-10” inconsistent: containing discrepancies in codes or names e.g., Age=“42” Birthday=“03/07/1997” e.g., Was rating “1,2,3”, now rating “A, B, C” e.g., discrepancy between duplicate records
Data Preparation in Strategic Business Intelligence
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August 10, 2015Data Mining: Concepts and Techniques 1Why Data
Preprocessing?Data in the rea !ord is dirtyinco"pete: ac#ing
attri$ute %aues, ac#ing certain attri$utes o& interest, or
containing ony aggregate datae'g', occupation() *noisy: containing
errors or outierse'g', +aary(),10*inconsistent: containing
discrepancies in codes or na"ese'g', Age()-2*
.irthday()0/00101221*e'g', Was rating )1,2,/*, no! rating )A, .,
C*e'g', discrepancy $et!een dupicate recordsAugust 10, 2015Data
Mining: Concepts and Techniques 2Why 3s Data Dirty?3nco"pete data
"ay co"e &ro")4ot appica$e* data %aue !hen coectedDi5erent
considerations $et!een the ti"e !hen the data !as coected and !hen
it is anay6ed'7u"an0hard!are0so&t!are pro$e"s4oisy data
8incorrect %aues9 "ay co"e &ro":auty data coection
instru"ents7u"an or co"puter error at data entry;rrors in data
trans"ission3nconsistent data "ay co"e &ro"Di5erent data
sources:unctiona dependency %ioation 8e'g', "odi&y so"e in#ed
data9Dupicate records aso need data ceaningAugust 10, 2015Data
Mining: Concepts and Techniques /Why 3s Data Preprocessing
3"portant?4o quaity data, no quaity "ining resuts
