A2 Clustring
Submission Report
Submit status
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I have started working on the assignment. -
All mandatory requirements for the grade I'm aiming for are met (see below). -
I have completed the assignment report (see below).
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I intend to submit the assignment, and at the same time, I guarantee that I am the one who created the submitted code. When I have used external libraries or used code from other sources, the source has been clearly stated.
(Swedish: Jag avser göra en inlämning av uppgiften och samtidigt garanterar jag att det är jag som skapat den inlämnade koden. När jag använt externa bibliotek, eller använt kod från andra källor, har källan angetts tydligt.)
Requirement fulfillment
Requirements
Grade E
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Implement K-means Clustering with Pearson similarity. -
Run the algorithm on the blog data dataset (see Datasets page) with five clusters. -
The iteration shall stop after a specified number of iterations. -
Present the result as a list of clusters and their assignments. -
Implement the system using a REST web service where: -
client sends a request to a server -
the server responds with JSON data -
the JSON data is decoded and presented in a client GUI
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Grade C-D
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Instead of stopping after a specified number of iterations, you shall implement functionality for stopping when no new assignments are made. -
Each cluster must keep track of the previous assignment, and a check is made if the new cluster assignment matches the previous one.
Grade A-B
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Implement Hierarchical Clustering with Pearson similarity. -
Run the algorithm on the blog data dataset. -
Present the result as an interactive tree in the client GUI (it shall be possible to expand/collapse branches).
Assignment report
This assigment is done on E level. You click on button on client and you will get 5 clouters with k means algorim in return.
Reflection
It was challeging in the begging its was difficult to understand K-means algoritm. It was difficult to understand the txt file dataset how to read it the right way.