Cleansing Data for Mining and Warehousing
| dc.contributor.author | Mong Li LEE | en_US |
| dc.contributor.author | Hongjun LU | en_US |
| dc.contributor.author | Tok Wang LING | en_US |
| dc.contributor.author | Yee Teng KO | en_US |
| dc.date.accessioned | 2004-10-21T14:28:52Z | en_US |
| dc.date.accessioned | 2017-01-23T06:59:51Z | |
| dc.date.available | 2004-10-21T14:28:52Z | en_US |
| dc.date.available | 2017-01-23T06:59:51Z | |
| dc.date.issued | 1999-06-01T00:00:00Z | en_US |
| dc.description.abstract | Given the rapid growth of data, it is increasingly important to extract, mine and discover useful information from databases and data warehouses. The process of data cleansing or data scrubbing is crucial because of the "garbage in, garbage out" principle. However, "dirty" data files are prevalent as a result of incorrect or missing data values due to data entry errors or typographical errors, inconsistent value naming conventions due to different field entry format and use of abbreviations, and incomplete information due to unavailability of data. Hence, we may have multiple records refering to the same real world entity. This not only contributes to the problem of handling ever-increasing amount of data, but also leads to the mining of inconsistent or inaccurate information which is obviously undesirable. In this paper, we examine the problem of detecting and removing duplicating records. We present several efficient techniques to pre-process the records before sorting them so that potentially matching records will be brought to a close neighbourhood subsequently. These techniques include scrubbing data fields using external source files to remove typographical errors and the use of abbreviations, tokenizing data fields and then sorting the tokens in the data fields. We also propose a method to determine the similarity between two records. Based on these techniques, we implement a data cleansing system which is able to detect and remove more duplicate records than existing methods. | en_US |
| dc.format.extent | 251580 bytes | en_US |
| dc.format.extent | 1569380 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.format.mimetype | application/postscript | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/1399 | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TRA6/99 | en_US |
| dc.title | Cleansing Data for Mining and Warehousing | en_US |
| dc.type | Technical Report | en_US |
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