Hi
I have done a clustering analysis using K means algorithm and I am wondering if anyone is up for adding a description of the method and some insight on how to interpret the results? Is need qute urgently as we wish to proceed quite soon. Please comment here or send PM for discussion.
Dear Hakan Lane
These links might be useful, have a look:
1. https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a
2. https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1
3. https://uc-r.github.io/kmeans_clustering
Kind Regards
Qamar Ul Islam
Dear Mr. Lane,
It is not clear, in what area you have used k-means algorithm. However the outputs must be clusters of the input data.
You can contact me for the interpretation at [email protected]
Yes interested I already worked on that where are you planning to publish it?
K-means is very well studied algorithm in many areas of sciences up to the level of maturity. I will suggest you to study it as your literature review together with data preprocessing techniques, e.g., dimensionality reduction for linear and nonlinear cases as well as for data mining view point. Next you can step forward to newly emerging algorithms for data analysis from various fields of your particular interest.
K- Means is unsupervised clustering method. It will process based upon centroid point. Once, the user defined the number of cluster ( 5, 6,) then K- Means segregated the give data set in to 5, 6.
K-means is an unsupervised learning algorithm having a pre-defined number of groups that is not useful and change the goal of k-means that provide an optimal number of clusters based on your datasets. Here you will have to group your data following value is taken by one variable. I suggest you ranking in this case
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