| 000 | 01867nam a2200253 a 4500 | ||
|---|---|---|---|
| 001 | vtls000067645 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204524.0 | ||
| 008 | 121211t2012 my da f m 000 0 eng d | ||
| 020 | _aTHE0002016(Local) | ||
| 039 | 9 |
_a201905131605 _byusri _c201301081435 _dhuda _y201212111651 _zhuda |
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| 040 | _aUMP | ||
| 090 | _aQA278 .R53 2012 rs Bc. | ||
| 100 | 0 | _aMohd Ridzuan Baharin | |
| 245 | 1 | 0 |
_aData clustering using min-min roughness and its application to cluster patients suspected diabetics / _cMohd Ridzuan Baharin |
| 260 |
_aKuantan, Pahang : _bUMP, _c2012 |
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| 300 |
_a102 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM |
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| 502 | _aProject paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang - 2012 | ||
| 504 | _aIncludes bibliographical references | ||
| 520 | 3 | _aIn the context of information technology nowadays, there are many data exists. All of this data are scrambled over inside the computer and with the presence of internet, even more data exist. The problem with this is, when we want the needed data only, there are too many to look for and they are all scrambled over the internet databases. Therefore, there are techniques that are proposed that will provide a way to automatically mine the data and obtain only meaningful data from the huge data over the internet. The area discussed in this research is Knowledge Discovery in Databases (KDD) and the technique used is Minimum-Minimum Roughness (MMR). The dataset used will be the dataset of diabetic patients. By using this MMR technique, I intended to cluster the diabetic dataset n which each cluster will contain the data most related to each other. | |
| 650 | 0 | _aCluster analysis | |
| 650 | 0 |
_aCluster analysis _xData processing |
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| 999 |
_aVIRTUA40 _c3493 _d3499 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*9992 | ||