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K-Means Clustering: EX - NO:6

The document outlines a procedure for performing K-means clustering using the WEKA data mining tool. It includes detailed steps for opening datasets, selecting attributes, running clustering algorithms, and visualizing data. The result indicates that the K-means clustering was successfully implemented using WEKA.

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logesh20bec
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10 views12 pages

K-Means Clustering: EX - NO:6

The document outlines a procedure for performing K-means clustering using the WEKA data mining tool. It includes detailed steps for opening datasets, selecting attributes, running clustering algorithms, and visualizing data. The result indicates that the K-means clustering was successfully implemented using WEKA.

Uploaded by

logesh20bec
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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EX.

NO:6
K-MEANS CLUSTERING
DATE:

AIM:

INTRODUCTION:
WEKA is a popular open-source tool for data mining and machine learning tasks.
While its primary use is for building and evaluating machine learning models, it can also
be used for data validation, which involves ensuring that the data you're working with is
of high quality, consistent, and suitable for analysis.

PROCEDURE:

STEP 1: Open Weka and select "Explorer," then the Preprocess page will appear as a pop-
up.
STEP 2: Click on "open file" option and do the following steps as per pervious
experiment to open the data files.

STEP 3: Select "data" from the list of folders to access the datasets for your project.
STEP 4: After selecting data, choose your desired project (e.g., iris) to open it in Weka
Explorer.

STEP 5: The project window will open in Weka Explorer, Select "All" from the attribute
list in the project to highlight all attributes in the dataset.
STEP 6: Select "None" from the attribute list in the iris project to the attributes in the
dataset.

STEP 7: Then the project window is opened and go to the cluster pop up
window. After enabling in the cluster mode run each set.
STEP 8: Select “use training set” option in the list and click start to run the code and
clusterer shows the output.
STEP 10: Select “supplied test set” option in the list and click start to run the code and
clusterer shows the output.
STEP 12: Select “percentage split” option in the list and click start to run the code and
clusterer shows the output.
STEP 14: Select “classes to clusters evaluation” option in the list and click start to run the
code and clusterer shows the output.
STEP 16: To visualize a specific part of the data, select "Visualize” For to choose the
subset of data to analyze

STEP 17: To visualize a specific part of the data, select "Visualize" for and increase the
jitter to make the data points more distinct.
STEP 18: Click the "Edit" button to open the data editor and modify the values.
STEP 19: Clicking "Visualize All" will show the visualization screen, giving a
graphical representation of your data.

STEP 20: Click the Yes button to confirm and save. Confirm by clicking the Yes button
in the dialog box.
RESULT:
Thus, The Apply WEKA tool for K-MEANS Clustering was Implemented
Successfully.

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