FCM 聚类数值数据和 csv/excel 文件

发布于 2024-12-09 01:42:43 字数 565 浏览 0 评论 0原文

您好，我问了一个上一个问题，它给出了合理的答案，我认为我回到了正轨，Matlab 中的模糊 c 均值 tcp 转储聚类问题是以下 tcp/udp 数据的预处理阶段，我想通过 matlabs fcm 聚类算法运行这些数据。我问题：

1）我如何或什么是将单元格中的文本数据转换为数值的最佳方法？数值应该是多少？

编辑：我在 Excel 中的数据现在如下所示：

在此处输入图像描述

0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,normal.

原文

Hi I asked a previous question that gave a reasonable answer and I thought I was back on track, Fuzzy c-means tcp dump clustering in matlab the problem is the preprocessing stage of the below tcp/udp data that I would like to run through matlabs fcm clustering algorithm.My question:

1) how do i or what would be the best method to convert the text data in the cells to a numeric value? what should the numeric value be?

Edit: My data in excel looks like this now:

enter image description here

0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,normal.

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挽梦忆笙歌 2024-12-16 01:42:43

下面是我如何将数据读入 MATLAB 的示例。您需要两件事：以逗号分隔格式的数据本身，以及特征列表及其类型（数字、标称）。

%# read the list of features
fid = fopen('kddcup.names','rt');
C = textscan(fid, '%s %s', 'Delimiter',':', 'HeaderLines',1);
fclose(fid);

%# determine type of features
C{2} = regexprep(C{2}, '.
您还可以查看统计工具箱中的 DATASET 类。
,'');              %# remove "." at the end
attribNom = [ismember(C{2},'symbolic');true]; %# nominal features

%# build format string used to read/parse the actual data
frmt = cell(1,numel(C{1}));
frmt( ismember(C{2},'continuous') ) = {'%f'}; %# numeric features: read as number
frmt( ismember(C{2},'symbolic') ) = {'%s'};   %# nominal features: read as string
frmt = [frmt{:}];
frmt = [frmt '%s'];                           %# add the class attribute

%# read dataset
fid = fopen('kddcup.data','rt');
C = textscan(fid, frmt, 'Delimiter',',');
fclose(fid);

%# convert nominal attributes to numeric
ind = find(attribNom);
G = cell(numel(ind),1);
for i=1:numel(ind)
    [C{ind(i)},G{i}] = grp2idx( C{ind(i)} );
end

%# all numeric dataset
M = cell2mat(C);

您还可以查看统计工具箱中的 DATASET 类。

Here is an example how I would read the data into MATLAB. You need two things: the data itself which is in comma-separated format, as well as the list of features along with their types (numeric,nominal).

%# read the list of features
fid = fopen('kddcup.names','rt');
C = textscan(fid, '%s %s', 'Delimiter',':', 'HeaderLines',1);
fclose(fid);

%# determine type of features
C{2} = regexprep(C{2}, '.
You could also look into the DATASET class from the Statistics Toolbox.
,'');              %# remove "." at the end
attribNom = [ismember(C{2},'symbolic');true]; %# nominal features

%# build format string used to read/parse the actual data
frmt = cell(1,numel(C{1}));
frmt( ismember(C{2},'continuous') ) = {'%f'}; %# numeric features: read as number
frmt( ismember(C{2},'symbolic') ) = {'%s'};   %# nominal features: read as string
frmt = [frmt{:}];
frmt = [frmt '%s'];                           %# add the class attribute

%# read dataset
fid = fopen('kddcup.data','rt');
C = textscan(fid, frmt, 'Delimiter',',');
fclose(fid);

%# convert nominal attributes to numeric
ind = find(attribNom);
G = cell(numel(ind),1);
for i=1:numel(ind)
    [C{ind(i)},G{i}] = grp2idx( C{ind(i)} );
end

%# all numeric dataset
M = cell2mat(C);

You could also look into the DATASET class from the Statistics Toolbox.

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