Peculiar data are regarded as patterns which are significantly distinguishable from other
records, relatively few in number and they are accepted as to be one of the most striking
aspects of the interestingness concept. In clinical domain, peculiar records are probably
signals for malignancy or disorder to be intervened immediately. The investigation of the
rules and mechanisms which lie behind these records will be a meaningful contribution for
improved clinical decision support systems.
In order to discover the most interesting records and patterns, many peculiarity oriented
interestingness measures, each fulfilling a specific requirement, have been developed. In this
thesis well-known peculiarity oriented interestingness measures, Local Outlier Factor (LOF),
Cluster Based Local Outlier Factor (CBLOF) and Record Peculiar Factor (RPF) are
compared. The insights derived from the theoretical infrastructures of the algorithms were
evaluated by using experiments on synthetic and real world medical data. The results are discussed based on the interestingness perspective and some departure points for building a
more developed methodology for knowledge discovery in databases are proposed.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609856/index.pdf |
| Date | 01 September 2008 |
| Creators | Aldas, Cem Nuri |
| Contributors | Taskaya Temizel, Tugba |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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