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edited Apr 13, 2017 at 12:44

Several points:

Ward method is geometrically correct to use only with a matrix of squared Euclidean distances. You may not apply it to cosines unless they are internally converted by the clustering program into those distances. So, you should read help docs of the program you use to know how it treats cosines when Ward is used. Anyway, Euclidean $d^2=2(1-cos)$, for your information.
Since Ward attempts to minimize within cluster sum of squared deviations it naturally follows that one should generally prefer an isomorphic clustering validation criterion - mean squared error. A number of popular clustering criterions are based on MSE, including famous Calinski-Harabasz criterion and Davies-Bouldin criterion. There exist many programs which compute them.
Clustering criterions should mostly be taken as relative measures only. That is, one compares alternative clusterings with their help. Absolute magnitude of a criterion is of little use. See further discussion of the topic here here.

Several points:

Ward method is geometrically correct to use only with a matrix of squared Euclidean distances. You may not apply it to cosines unless they are internally converted by the clustering program into those distances. So, you should read help docs of the program you use to know how it treats cosines when Ward is used. Anyway, Euclidean $d^2=2(1-cos)$, for your information.
Since Ward attempts to minimize within cluster sum of squared deviations it naturally follows that one should generally prefer an isomorphic clustering validation criterion - mean squared error. A number of popular clustering criterions are based on MSE, including famous Calinski-Harabasz criterion and Davies-Bouldin criterion. There exist many programs which compute them.
Clustering criterions should mostly be taken as relative measures only. That is, one compares alternative clusterings with their help. Absolute magnitude of a criterion is of little use. See further discussion of the topic here.

Several points:

Ward method is geometrically correct to use only with a matrix of squared Euclidean distances. You may not apply it to cosines unless they are internally converted by the clustering program into those distances. So, you should read help docs of the program you use to know how it treats cosines when Ward is used. Anyway, Euclidean $d^2=2(1-cos)$, for your information.
Since Ward attempts to minimize within cluster sum of squared deviations it naturally follows that one should generally prefer an isomorphic clustering validation criterion - mean squared error. A number of popular clustering criterions are based on MSE, including famous Calinski-Harabasz criterion and Davies-Bouldin criterion. There exist many programs which compute them.
Clustering criterions should mostly be taken as relative measures only. That is, one compares alternative clusterings with their help. Absolute magnitude of a criterion is of little use. See further discussion of the topic here.

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created Mar 27, 2013 at 9:34

Several points:

Ward method is geometrically correct to use only with a matrix of squared Euclidean distances. You may not apply it to cosines unless they are internally converted by the clustering program into those distances. So, you should read help docs of the program you use to know how it treats cosines when Ward is used. Anyway, Euclidean $d^2=2(1-cos)$, for your information.
Since Ward attempts to minimize within cluster sum of squared deviations it naturally follows that one should generally prefer an isomorphic clustering validation criterion - mean squared error. A number of popular clustering criterions are based on MSE, including famous Calinski-Harabasz criterion and Davies-Bouldin criterion. There exist many programs which compute them.
Clustering criterions should mostly be taken as relative measures only. That is, one compares alternative clusterings with their help. Absolute magnitude of a criterion is of little use. See further discussion of the topic here.