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I'm looking for a decent implementation of KNN algorithm in java because, in my dissertation, I have to modify it using different data structures.
Thanks in advance!
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It's main disadvantages are that it is quite computationally inefficient and its difficult to pick the “correct” value of K. However, the advantages of this algorithm is that it is versatile to different calculations of proximity, it's very intuitive and that it's a memory based approach.
Why is the k-nearest neighbors algorithm called “lazy”? Because it does no training at all when you supply the training data. At training time, all it is doing is storing the complete data set but it does not do any calculations at this point.
The k-nearest neighbors algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.
Here is full implementation and description.
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