Separation of mixed data sets into homogeneous sets /
Harold L. Crutcher, Raymond L. Joiner, United States. · 1977
In any study, the collection, processing, and storage of data are important.Whether the data are clean, biased or contaminated is also important.Pollution or adulteration of data confuse the investigator.Data do not necessarily fall into neatly packaged boxes or groups.Usually the data sets are mixtures of several types of phenomena.Some of these are basically deterministic in nature while others are not.This paper illustrates the use of a clustering technique to separate mixed data sets into subsets which exhibit group characteristics.The investigator then assesses the relative importance of the subsets, the nature of the sub- sets, and perhaps makes an assumption as to whether a particular subset is biased, contaminated, or adulterated.That is, an assessment of the quality of the data may be made.The techniques are applicable to any data set which is multivariate normal.Here, they are applied to weather data subsets, (1) land-sea breeze, (2) tropical stratospheric winds, (3) mid-latitude tropospheric winds, (4) mountain pass winds and temperatures, (5) surface marine weather temperatures, dew points and winds, and (6) radio- sonde observation of heights, winds, temperatures, and dew points.1. 8. EXAMPLES 8.1 *00 0.207 0.793 *01 0.02* 0.976 *02 0