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clustering and matlab

Visits(95) Posted by AmroGarrith Graham 3.2k

Hi im trying to cluster some data I have from the kdd 1999 cup dataset

the output from the file looks like this:

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.

with 48 thousand different records in that format. I have cleaned the data up and removed the text keeping only the numbers. The output looks like this now:

I created a comma dillemated file in excel and saved as a csv file then created a data source from the csv file in matlab, ive tryed running it through the fcm toolboxin matlab (findcluster outputs 38 data types which is expected with 38 columns).

The clusters however dont look like clusters or its not accepting and working the way I need it to.

Could anyone help finding the clusters? Im new to matlab so dont have any experience and Im also new to clustering.

The method:

1. Chose number of clusters (K)2. Initialize centroids (K patterns randomly chosen from data set)

3. Assign each pattern to the cluster with closest centroid4. Calculate means of each cluster to be its new centroid5. Repeat step 3 until a stopping criteria is met (no pattern move to another cluster)

This is what I'm trying to achieve:

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This is what im getting:

load kddcup1.datplot(kddcup1(:,1),kddcup1(:,2),'o') [center,U,objFcn] = fcm(kddcup1,2); Iteration count = 1, obj. fcn = 253224062681230720.000000 Iteration count = 2, obj. fcn = 241493132059137410.000000 Iteration count = 3, obj. fcn = 241484544542298110.000000 Iteration count = 4, obj. fcn = 241439204971005280.000000 Iteration count = 5, obj. fcn = 241090628742523840.000000 Iteration count = 6, obj. fcn = 239363408546874750.000000 Iteration count = 7, obj. fcn = 238580863900727680.000000 Iteration count = 8, obj. fcn = 238346826370420990.000000 Iteration count = 9, obj. fcn = 237617756429912510.000000 Iteration count = 10, obj. fcn = 226364785036628320.000000 Iteration count = 11, obj. fcn = 94590774984961184.000000 Iteration count = 12, obj. fcn = 2220521449216102.500000 Iteration count = 13, obj. fcn = 2220521273191876.200000 Iteration count = 14, obj. fcn = 2220521273191876.700000 Iteration count = 15, obj. fcn = 2220521273191876.700000

figureplot(objFcn) title('Objective Function Values') xlabel('Iteration Count') ylabel('Objective Function Value')

maxU = max(U); index1 = find(U(1, :) == maxU); index2 = find(U(2, :) == maxU); figureline(kddcup1(index1, 1), kddcup1(index1, 2), 'linestyle',... 'none','marker', 'o','color','g'); line(kddcup1(index2,1),kddcup1(index2,2),'linestyle',... 'none','marker', 'x','color','r'); hold onplot(center(1,1),center(1,2),'ko','markersize',15,'LineWidth',2)

plot(center(2,1),center(2,2),'kx','markersize',15,'LineWidth',2)

Answer (1) matlab machine-learning cluster-analysis data-mining fuzzy

# 1

Answered by Amro

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Since you are new to machine-learning/data-mining, you shouldn't tackle such advanced problems. After all, the data you are working with was used in acompetition (KDD Cup'99), so don't expect it to be easy!

Besides the data was intended for a classification task (supervised learning), where the goal is predict the correct class (bad/good connection). You seem to beinterested in clustering (unsupervised learning), which is generally more difficult.

This sort of dataset requires a lot of preprocessing and clever feature extraction. People usually employ domain knowledge (network intrusion detection) to obtain better features from the raw data.. Directly applying simple algorithms like K-means will generally yield poor results.

For starters, you need to normalize the attributes to be of the same scale: when computing the euclidean distance as part of step 3 in your method, the features withvalues such as 239 and 486 will dominate over the other features with small values as 0.05, thus disrupting the result.

Another point to remember is that too many attributes can be a bad thing (curse of dimensionality). Thus you should look into feature selection or dimensionality

reduction techniques.

Finally, I suggest you familiarize yourself with a simpler dataset...

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88. Group n points in k clusters of equal size [duplicate]Possible Duplicate: K-means algorithm variation with equal cluster size EDIT: like casperOne point it out to me this question is a duplicate. Anyways here is amore generalized question that cover this one: http://stats.stackexchange.com/questions/8744/cl...

89. Friend GroupingI am writing a program that fetches the links between friends on facebook and then create friendship groups from these links. I have got as far as creating thedata structure which is something like [ friend_id:[ mutual_friend_id, mutual_friend_id, mutual...

90. Java library method or algorithm to estimate aggregate string similarity?I have responses from users to multiple choice questions, e.g. (roughly): Married/SingleMale/FemaleAmerican/Latin American/European/Asian/African WhatI want is to estimate similarity by aggregating all responses into a single field which can be compared ...

91. Markov Clustering AlgorithmI've been working through the following example of the details of the Markov Clustering algorithm: http://www.cs.ucsb.edu/~xyan/classes/CS595D-2009winter/MCL_Presentation2.pdf I feel like I have accurately represented the algorithm but I am not getting th...

92. Clustering a list using boundary functionGiven a list, I'd like to divide it into clusters using a "boundary function". Such function would take two consecutive elements of the list and decide whether or not they should belong to the same cluster. So essentially, I want something like this: clus...

93. Get point IDs after clustering, using python [duplicate]Possible Duplicate: Python k-means algorithm I want to cluster 10000 indexed points based on their feature vectors and get their ids after clustering i.e.cluster1:[p1, p3, p100, ...], cluster2:[...] ... Is there any way to do this in Python? Thx~ P.s. Th...

94. Clustering a sparse dataset of binary vectorsIf I have a sparse dataset where each data is described by a vector of 1000 elements, each element of this vector can be either 0 or 1 (a lot of 0 and some 1), doyou know any distance function that could help me to cluster them ? Is something like euclid...

95. R: Unused argument “label” in hclustI'm using the following code to build and hierarchical cluster: dat - read.table(textConnection("pdb PA EHSS 1avd_model.pdb 3028.0 3920.01ave_model.pdb3083.0 4019.01ij8_model.pdb 2958.0 3830.01ldo_model.pdb 2889.0 3754.01ldq_model.pdb 2758.0 3590.01lel_m...

96. Clustering algorithm to cluster objects based on their relation weightI have n words and their relatedness weight that gives me a n*n matrix. I'm going to use this for a search algorithm but the problem is I need to cluster the

entered keywords based on their pairwise relation. So let's say if the keywords are tennis,feder...97. How to best do server-side geo clustering?

I want to do pre-clustering for a set of approx. 500,000 points. I haven't started yet but this is what I had thought I would do: store all points in a localSOLR index determine "natural cluster positions" according to some administrative information (big...

98. Same result from K-means and sequential K-means?Do we obtain the same result if we apply K-means and sequential K-means methods to the same dataset with the same initial settings? Explain your reasons.Personally I think the answer is No. The result obtained by sequential K-means depends on the present...

99. Excel 2010 - Create Cluster GraphIs there a way to create cluster graphs within Excel 2010? More specifically, I am looking for the type of cluster graph which resembles a scatter graph asopposed to a bar chart. I am working with k-means and the best I can achieve in Excel at the moment...

100. Google Maps Clustering Markersi have a list of markers but i want to change them as address. var data = "loc": ["longitude": -81.81718856098772, "latitude": 26.278657439364583 ,"longitude": -81.81291211952795, "latitude": 26.199298735114475,"longitude": - 81.74875180993064, "lat...

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