Algorithms for Clustering Data by Anil K. Jain

By Anil K. Jain

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O. )RMAT(16I5) Always punched; the array MBEST contains NVAR integers indi cating whi ch variables gave the best residual sum of squares found yet. The purpose of these cards is to permit a search for the optimal result to be continued at the very point where it was stopped because of an excessive number of iterations. Also, it permits previous optimal results to be printed in the same out- put with new results without the program searching again for the optimal solution. Since the computer punches a set of cards in the correct order, the user need only place the sets in the same order as specified on equations previously processed card.

Problems with many independent variables; This situation becomes acute for large as the number of variables desired in the equation increases, computation can be excessively time consuming and the results hardly worth the computer time. Four procedures implemented to alleviate this situ- ation are now considered. First, ·the user can specify each value of N for which a regression is desired. In most large problems, the level of confidence associated to the F-ratio first increases, and then decreases with N.

3. Card type 17 seems to be incorrect or mixed up. For every regression problem with a given set of variables, the following card types will always be necessary: iteration limits; title; problem definition; equations desired; output options. If a set of data has been read in earlier in this job, and is to be used again during the same job, a format of data card and data deck will not be needed if the user sets NAGAIN = 1 and NFMT = O. However, for all other regression problems, a data deck and card(s) to specify its format are required.

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