TY - GEN
T1 - Importance of data standardization in privacy-preserving k-means clustering
AU - Su, Chunhua
AU - Zhan, Justin
AU - Sakurai, Kouichi
PY - 2009
Y1 - 2009
N2 - Privacy-preserving k-means clustering assumes that there are at least two parties in the secure interactive computation. However, the existing schemes do not consider the data standardization which is an important task before executing the clustering among the different database. In this paper, we point out without data standardization, some problems will arise from many applications of data mining. Also, we provide a solution for the secure data standardization in the privacy-preserving k-means clustering.
AB - Privacy-preserving k-means clustering assumes that there are at least two parties in the secure interactive computation. However, the existing schemes do not consider the data standardization which is an important task before executing the clustering among the different database. In this paper, we point out without data standardization, some problems will arise from many applications of data mining. Also, we provide a solution for the secure data standardization in the privacy-preserving k-means clustering.
UR - http://www.scopus.com/inward/record.url?scp=70349315115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349315115&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04205-8_23
DO - 10.1007/978-3-642-04205-8_23
M3 - Conference contribution
AN - SCOPUS:70349315115
SN - 364204204X
SN - 9783642042041
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 276
EP - 286
BT - Database Systems for Advanced Applications - DASFAA 2009 International Workshops
T2 - International Workshops on Database Systems for Advanced Applications, DASFAA 2009: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD
Y2 - 20 April 2009 through 23 April 2009
ER -