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Part 1:

– Writing and testing your own implementation of k-means on a simple data (IRIS) (Follow the description attached for more details)

Part 2:

– The MNIST data is provided under

Test

and consists of 10,000 images of handwritten digits (0-9). The images were scanned and scaled into 28×28 pixels. For every digit, each pixel can be represented as an integer in the range [0, 255] where 0 corresponds to the pixel being completely white, and 255 corresponds to the pixel being completely black. This gives us a 28×28 matrix of integers for each digit. We can then flatten each matrix into a 1×784 vector. No labels are provided.

– Use your algorithm from Part 1 to assign each of the instances in the input data to K clusters identified from 1 to K

– The resulting file contains 1 collum of 10,000 rows with int from 1 to 10.

Iris data:







https://archive.ics.uci.edu/ml/machine-learning-da…


MNIST data: Attachement (testData)

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