Mendeley
TY  _  JOUR
ID  -  13960423212306973
TI  -  Automatic Error Detecting in Databases, Based on Clustering and Nearest Neighbor
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  ataeyan M.
AU  -  Daneshpour Negin
AD  -  دانشگاه تربیت دبیر شهید رجايي
AD  -  عضو هیات علمی دانشگاه
Y1  -  2017
PY  -  2017
VL  -  4
IS  -  
SP  -  349
EP  -  356
KW  -  Data cleaning
automatic error detection
clustering
k-means
DO  -  
N2  -  Data quality affects on companies decision making, so that decisions based on data without quality incur companies high costs. Data quality has various dimensions and accuracy is the most important of these dimensions. Error detection is needed for data cleaning. Due to the huge volume of data, an automatic system is needed to perform this process without user interaction. In this paper an approach is proposed based on k-means clustering for error detection. Firstly data are clustered for each attribute. Then for each data in each cluster a method similar to k-nearest neighbor is used for detecting errors. The proposed method is able to detect multiple errors in one record. Also this approach is able to detect errors in fields with various attribute types. Experimental results show that this approach can detect 91% of errors in data on average. Also the proposed approach is compared with an automatic method which detects errors based on rule in various attribute types. Experimental results show that the proposed approach has on average 25%better performance to detect errors.
UR  - rimag.ir/en/Article/28223
L1  - rimag.ir/en/Article/Download/28223
ER  -  
