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In-Depth Information
In cancer research, data mining has been applied to cluster different
types of cancer cells according to hundreds or thousands of attributes
on the cancer cells. This data can consist of visible aspects of the can-
cers, as well as gene-level data. Those cancer cells that appear in the
same cluster as cancer cells with known treatments may be treated
similarly. Accurately diagnosing cancer in patients is essential for
selecting optimal treatment. However, accurate diagnosis is often diffi-
cult since tumor appearance and location are not always sufficient to
properly classify a tumor. Moreover, clinical data can be incomplete or
misleading. Data mining has also been successfully employed to reduce
error by using molecular classification of common adult malignancies.
Using microarray-based tumor gene expression profiles, classification
techniques using data from over sixteen thousand genes have been
analyzed to yield more accurate diagnoses [Ramaswamy
2001].
For drug discovery and drug interactions, data mining is being
employed “to predict properties such as absorption rates, metabo-
lites, liver toxicity, and carcinogenicity” [Pinsky 2005].
2.3
Summary
In this chapter, we identified and discussed several cross-industry
solutions where data mining plays a central role. Understanding
such common data mining scenarios is a beginning for identifying
uses of data mining in your individual application domains. We also
highlighted several industries and their particular uses of data
mining. Each of these industries can apply the cross-industry
solutions cited, tailored to its own domain-specific needs.
We have so far discussed various mining techniques at a high
level. In the next chapter, we go to the next level of detail, discussing
data mining functions and algorithms that are provided in JDM.
References
[Apte
2002] C. Apte, B. Liu, E. Pednault, P. Smyth, “Business Applications
of Data Mining,” Communications of the ACM , vol. 45, no. 8, August
2002.
[Bakin
1999] http://citeseer.ifi.unizh.ch/bakin99mining.html.
[Berry/Linoff 2004] M. Berry, G. Linoff, Data Mining Techniques for
Marketing, Sales, and Customer Relationship Management , New York, John
Wiley & Sons, Inc., 2004.
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