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[Octave-bug-tracker] [bug #49411] fcm problem with clustering big data s
From: |
Tony Trew |
Subject: |
[Octave-bug-tracker] [bug #49411] fcm problem with clustering big data sets |
Date: |
Sat, 22 Oct 2016 07:15:52 +0000 (UTC) |
User-agent: |
Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36 |
URL:
<http://savannah.gnu.org/bugs/?49411>
Summary: fcm problem with clustering big data sets
Project: GNU Octave
Submitted by: tonytrew
Submitted on: Sat 22 Oct 2016 07:15:50 AM GMT
Category: Octave Forge Package
Severity: 3 - Normal
Priority: 5 - Normal
Item Group: Crash
Status: None
Assigned to: None
Originator Name: Tony Trew
Originator Email:
Open/Closed: Open
Discussion Lock: Any
Release: 4.0.2
Operating System: Microsoft Windows
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Details:
Using fcm in the fuzzy-logic-toolkit-0.4.5 runs into problems with bigger sets
of data points. This is the function call:
[cluster_centers, soft_partition, obj_fcn_history]=fcm(Xpc,k,[2,100,1e-4]);
Xpc (size 22613 X 14) is a set of election results in percentages with each
point summing to 1.00
The script terminates at the first iteration with the message:
Iteration count = 1, Objective fcn = NaN
If the number of points is reduced to 2700 it completes and returns
[cluster_centers, soft_partition, obj_fcn_history], as well as values for:
partition_coeff (soft_partition));
partition_entropy (soft_partition, 2));
xie_beni_index (Xpc1, cluster_centers, soft_partition));
But any more points than 2700 and the same problem occurs, even if the number
of clusters is reduced to 2 and the dimension of the data points decreased.
I am running this on an HP laptop, with 8.00 Gb RAM, 64-bit operating system ,
x64 based processor
_______________________________________________________
Reply to this item at:
<http://savannah.gnu.org/bugs/?49411>
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