YCSCYale Child Study Center
YCSCYMCA (Young Men's Christian Association) Columbia Swim Club (South Carolina)
YCSCYouth Coalition Santa Cruz (Santa Cruz, CA)
YCSCYoga Center Santa Cruz (Santa Cruz, CA)
YCSCYale Club of Southern California
YCSCYouthBuild Charter School of California
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References in periodicals archive ?
In this paper we show how the same mechanisms can be used within YCSc to identify clusters within a given dataset; the set pressure encourages the evolution of rules which cover many data points (via [[theta].sub.GA]) and the fitness pressure acts as a limit upon the separation of such data points, i.e., the error (via [upsilon]).
In this section we apply YCSc as described above on two datasets for the first experiment to test the performance of the system.
Figure 2 shows a typical example solutions produced by YCSc on both data sets.
YCSc's identification of the clusters is now clear.
In the less-separated case there is no significant difference in performance between YCSc and k-means.
We have begun to examine the performance of YCSc compared to k-means over randomly generated datasets in several d dimensions with varying numbers k clusters.
Table 1 shows how YCSc always gives superior quality and gives an equivalent or closer estimate of the number of clusters compared to k-means.
Thus far YCSc has struggled with the lessseparated data.
Figure 8 shows typical solutions produced by YCSc with local search using the fitness function in equation 6.