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'Intelligent Yardstick', An Approach Of Ranking To Filter NonPromising Attributes From Schema In Data Mining Process Hassan, MM SPRINGER-VERLAG BERLIN, INTELLIGENT CONTROL AND AUTOMATION; pp: 623-632; Vol: 344 King Fahd University of Petroleum & Minerals http://www.kfupm.edu.sa Summary Knowledge searching and representation process needs to go through filtering to make it concise and precise. The data mining process (DM) as a knowledge extraction method also needs filtering to remove unnecessary or erroneous data. Sometimes filtering also needs to cover processing or representational limits, especially in visual data mining. Common approaches for filtering or shrinking the database are - discard records, prune attributes from the database or reduce dimensionality. In this paper we propose an approach where we have applied single dimension DM process on each individual attribute to select only those attributes which are good for future full-scale DM and prune the non prospective attributes. In this process we check several properties of a single attribute like data distance, clustering tendency and cluster density. According to the result, we formulate a scale that will rank all attributes to indicate their importance for future knowledge extraction schema. The main observation that helps us to build this approach is -'An attribute that has no data pattern itself will not help to find a pattern in a multidimensional environment'. References: 1. 2. 3. 4. 5. 6. 7. © ABRAHAM B, 2005, IEEE T KNOWLEDGE DAT, V17 EAMONN K, 2002, ACM T DATABASE SYST, V27, P188 FAYYAD UM, 1996, DATA MIN KNOWL DISC, P1 HINNEBURG A, 2003, COMPUT SCI ENG, V5, P14 MARK AH, 2005, IEEE T KNOWLEDGE DAT, V17 MOHAMMAD MH, P INT C INT SYST KUA PAUL A, 2002, IEEE T KNOWLEDGE DAT, V14 Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa 8. 9. 10. 11. 12. RAKESH A, 1993, IEEE T KNOWL DATA EN, V5, P914 SAMUEL HH, 2003, IEEE T KNOWLEDGE DAT, V15 STUART R, 2003, STAT LEARNING METHOD, P736 WYNNE H, 2000, P 6 ACM SIGKDD INT C, P430 XINGQUAN Z, 2005, IEEE T KNOWLEDGE DAT, V17 For pre-prints please write to: [email protected] © Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa
