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With partitioned-based clustering (also referred to as hard or crisp clustering), each observation belongs to a single cluster, there are no empty clusters, and the number of clusters needs to be speci ed prior to clustering. Like partitioned-based approaches, fuzzy clustering requires the number of clusters to be speci ed prior to clustering. However, unlike partitioned-based clustering, each observation belongs to all clusters. Accompanying each observation is a number that re ects the degree to which the observation belongs to each cluster. This membership is calculated using a membership function whereby the degree of association is re ected in a number between 0 and 1. Lower numbers indicate that the observation is marginally associated with the cluster, and higher numbers show the observation is strongly associated with a particular cluster. The results of fuzzy clustering are a membership matrix composed of k columns corresponding to the number of clusters, and n observations, as shown in Table 3.29. This membership matrix was calculated from the observations in Table 3.26. In the example in Table 3.29, ve observations (A, B, C, D, and E) have been clustered into three groups (cluster 1, cluster 2, and cluster 3). The degree of association with each cluster is shown. Observation A has almost no association with cluster 1 and cluster 2; however, it has a strong association with cluster 3. Observation C is most strongly associated with cluster 3, but it also has some level of association with cluster 1 and cluster 2. For each observation, the sum of all cluster associations for each observation is 1. how to create barcodes in excel 2010 free Get Barcode Software - Microsoft Store
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To insert bar codes into a Microsoft Excel document please follow these steps: barcode inventory excel program Creating a Barcode Add-In for Excel Spreadsheets - IDAutomation
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