The arboretum procedure
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- Bu səhifədəki naviqasiya:
- Current Frequency
- Distance
- Current Summarized
- Example Example 1: Syntax for PROC FASTCLUS Example 2: Use of the EMCLUS Procedure
- Example 1: Syntax for PROC FASTCLUS PROC EMCLUS
- Example 2: Use of the EMCLUS Procedure
- The NEURAL Procedure Overview Procedure Syntax
- Details Examples
The EMCLUS Procedure Output from PROC EMCLUS The beginning of the output shows the initial model parameter estimates. Next, the estimated model parameters, sample means, and sample variances for the active primary clusters are displayed. In active clusters are shown with missing values. The sample mean and variance are calculated from the observations that are summarized in the primary clusters. In the cluster summary table, the following statistics are listed: Current Frequency the number of observations that are summarized in a cluster during the current iteration. Total Frequency the cumulative sum of the current frequencies for each cluster. Proportion of Data Summarized the total frequency divided by the Obs read in. Nearest Cluster the closest primary cluster to a primary cluster based on the euclidean distance between the estimated mean of the two primary clusters.
the euclidean distance of a primary cluster to its nearest cluster. The iteration summary table displays:
the average log-likehood over all the observations that are read in. Obs read in this iteration the number of observations that are read in at current iteration. Obs read in the cumulative sum of observations that are read in. Current Summarized is the sum of the current frequencies across the primary clusters. Total Summarized is the sum of the total frequencies across the primary clusters. Proportion Summarized the Total Summarized divided by the Obs read in. If there are secondary clusters, the sample mean, sample variance, and the number of observations in secondary clusters are also displayed after the iteration summary table. Note: The estimated variance parameter for each variable is bound from below by the value (var)*(eps), where var is the sample variance of that variable obtained from the observations read in at the first iteration, and eps is 10 --6 . Both the standard and scaled EM algorithm sometimes are slow to convergence, however, the scaled EM algorithm generally runs faster than the standard EM algorithm. Convergence may be sped up by increasing p and /or eps, or by using the CLEAR option. Changing these values may alter the parameter estimates. Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. The EMCLUS Procedure Example Example 1: Syntax for PROC FASTCLUS Example 2: Use of the EMCLUS Procedure Chapter Contents Previous Next
Top of Page Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. The EMCLUS Procedure Example 1: Syntax for PROC FASTCLUS PROC EMCLUSlibref.SAS-data-set> OUTSEEDS = libref.SAS-data-set MAXCLUSTERS = positive integer; Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. The EMCLUS Procedure Example 2: Use of the EMCLUS Procedure PROC FASTCLUS returns a portion of the total output summarized in the following table: Cluster Frequency RMS Std.
Deviation 1 500 101.34 2 3 1000.34 3 100 3.79 4 150 4.05 Clusters 1, 3, and 4 should be used as initial estimates for PROC EMCLUS, because of their high frequency counts. Cluster 1 may actually be a group of clusters because of its high RMS Std. Deviation. Therefore, the syntax when using PROC EMCLUS could look like: PROC EMCLUS DATA= CLUSTERS = 5 INIT = FASTCLUS SEED = INITSTD = 50.0 INITCLUS 1, 3, 4; run; Note that CLUSTERS is set to 5, but any integer greater than or equal to 3 is appropriate since there are three clusters specified in the INITCLUS option. Also the INITSTD could have been set to any number less than 101.34 and greater than 4.05. Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The NEURAL Procedure The NEURAL Procedure Overview Procedure Syntax PROC NEURAL Statement ARCHITECTURE Statement CODE Statement CONNECT Statement CUT Statement DECISION Statement DELETE Statement FREEZE Statement FREQ Statement HIDDEN Statement INITIAL Statement INPUT Statement NETOPTIONS Statement NLOPTIONS Statement PERTURB Statement PRELIM Statement QUIT Statement RANOPTIONS Statement SAVE Statement SCORE Statement SET Statement SHOW Statement TARGET Statement THAW Statement TRAIN Statement USE Statement ACTIVATION FUNCTIONS COMBINATION FUNCTIONS Details Examples Example 1: Developing a Simple Multilayer Perceptron (Rings Data) Example 2: Developing a Neural Network for a Continuous Target Example 3: Neural Network Hill-and-Plateau Example (Surf Data) References Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. Yüklə 3,07 Mb. Dostları ilə paylaş:
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