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number of clusters and assigns observations to them. There are no overlaps in the groups, that is, all observations are assigned to a single group. This approach is computationally faster and can handle greater numbers of observations than agglomerative hierarchical clustering. However, there are a number of disadvantages: Prede ned number of clusters: You must de ne the number of groups before creating the clusters. Distorted by outliers: When a data set contains many outliers, k-means clustering may not create an optimal grouping. This is because the outliers

19: CORBA Applications in the Enterprise The retrieveData() method retrieves all the records present in the table. It gets a connection to the table by calling the getConn() method and creates a Statement object. As this method has to create a list of all the records present in the weather table, the SELECT query is built accordingly and given to the Statement object. The results returned by this query are stored in a ResultSet object. The weather object is created to hold a single record of the weather table. The ResultSet object is traversed to read the records one by one so that the values can be stored in a weather object. This weather object is added into a vector component. Because the number of records is known only at runtime, you use a vector component to store the weather objects. Once all the weather objects are stored in the vector component, an array of weather objects is created by giving the vector component's size as the size of the array. Then the contents of the vector component are copied into this array, and it is sent as a return value. Let me explain the next method, findDataCityMethod : public WeatherApp.WeatherObjectPackage.Weather[] findDataCity(String city). The method findDataCity() retrieves all the weather details about a city, which is passed as an input parameter. The method is defined as follows:

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1 Clark, R J H and Hester, R E, Biomedical Applications of Spectroscopy, Wiley, Chichester, UK, 1996 2 Gremlich, H-U and Yan, B, Infrared and Raman Spectroscopy of Biological Materials, Marcel Dekker, New York, 2000 3 Jackson, M, Moore, D J, Mantsch, H H and Mendelsohn, R, Vibrational Spectroscopy of Membranes , in Handbook of Vibrational Spectroscopy, Vol 5, Chalmers, J M and Grif ths, P R (Eds), Wiley, Chichester, UK, 2002, pp 3508 3518 4 Kalasinsky, V F, Appl Spectrosc Rev, 31, 193 249 (1996) 5 Mantsch, H H and Chapman, D (Eds), Infrared Spectroscopy of Biomolecules, Wiley, New York, 1996 6 Naumann, D, Appl Spectrosc Rev, 36, 239 298 (2001) 7 Petrich, W, Appl Spectrosc Rev, 36, 181 237 (2001) 8 Shaw, R A and Mantsch H H.

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, Vibrational Spectroscopy Applications in Clinical Chemistry , in Handbook of Vibrational Spectroscopy, Vol 5, Chalmers, J M and Grif ths, P R (Eds), Wiley, Chichester, UK, 2002, pp 3295 3307 9 Shaw, R A and Mantsch H H, Infrared Spectroscopy in Clinical and Diagnostic Analysis , in Encyclopedia of Analytical Chemistry, Vol 1, Meyers, R A (Ed), Wiley, Chichester, UK, 2000, pp 83 102 10 Stuart, B H, Biological Applications of Infrared Spectroscopy, ACOL Series, Wiley, Chichester, UK, 1997 11 Stuart, B H, Infrared Spectroscopy of Biological Applications , in Encyclopedia of Analytical Chemistry Vol 1, Meyers, R A (Ed), Wiley, Chichester, UK, 2000, pp 529 559 12 Watts A and De Pont J J H H M (Eds), Progress in Protein Lipid Interactions, 2, Elsevier, Amsterdam, The Netherlands, 1986 13 Lewis, R N A H and McElhaney, R N.

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