xingquan hill zhu fvenocsuvmedu apologies crosspostings please send interested colleagues research groups area call papers knowledge information systems kais special issue mining lowquality data httpwwwcsuvmeduxqzhukaiskais_mldhtm knowledge discovery data mining kdd dedicated exploring meaningful information large volume data realworld data rarely perfect often suffer corruptions may impact interpretations data models created data decisions made data general consensus among data mining practitioners low data quality often leads wrong decisions even ruins projects garbage garbage proper preprocessing techniques adopted advance data mining increasingly recognized key technology analyzing understanding data need knowledge discovery realworld lowquality data becomes overwhelming compelling result data quality related issues become crucial consumed majority time budget data mining mining lowquality data requires systematic efforts data quality management data preparation actual mining procedures previous research endeavors mainly focused separate part existing efforts data mining procedures often isolated data quality management data preprocessing modules therefore awareness underlying data quality addition since data preprocessing techniques result perfect data noise tolerant mining approach always preferred many realworld applications regardless whether data preparation data quality management modules incorporated bridge gap data quality management data preparation mining procedures need systematic research unify three components better performances special issue intended provide focal point research advances knowledge discovery lowquality data solicit highquality original papers address issues related data quality data mining including limited following topics data quality management data information quality assessment data quality models theoretical foundations domainspecific data quality evaluation metrics quality characterization data quality monitoring data enhancement data cleansing noise detection data correction resolving data redundancy inconsistency duplicates conflicts datafeature reduction instancefeature ranking data editing imputation data acquisition filling incomplete values data transformations reconciliation consolidation intelligent tools data enhancement data mining algorithms lowquality data integrating data quality measures effective mining quality awareness data mining noise tolerant data mining submission guidelines strongly encourage authors submit manuscripts pdf journal submission website httpwwwcsuvmedukais via email xqzhucsuvmedu please mention special issue submission remarks important dates manuscript submission may 1 2005 acceptance notification september 1 2005 final manuscript due september 30 2005 publication 1st half 2006 special issue guest editors dr xingquan zhu fvenocsuvmedu university vermont dr ian davidson ltuesssicsalbanyedu state university new york albany dr shichao zhang olkjgquitutseduau university technology sydney dr taghi khoshgoftaar qomdlcsefauedu florida atlantic university please refer special issue website httpwwwcsuvmeduxqzhukaiskais_mldhtm details _______________________________________________ tue 05 aug 2008 193514 0800 uai cfp kais special issue mining lowquality data