Validating in algorithm
I'm using scikit-learn's affinity propagation clustering against a dataset composed of objects with many attributes.The difference matrix supplied to the clustering algorithm is composed of the weighted difference of these attributes.And The process of measuring the effectiveness of an algorithm before it is coded to know the algorithm is correct for every possible input. Example :- This article describes the algorithms for validating bank routing numbers and credit card numbers using the checksum built into the number.While they differ in how they are generated, the technique used for both is similar.I have written a basic search tree, but never heard of this concept."Another solution(if space is not a constraint) Do an inorder traversal of the tree and store the node values in an array.For clustering to make sense for an application, you first need to think about the specifications.Most algorithms have some more or less explicit specifications, and people care much too little about them. It has the key assumptions that A) the mean is a sensible representative of the cluster and that B) variance must be minimized.
I read on here of an exercise in interviews known as validating a binary search tree. What would one be looking for in validating a binary search tree?
It too will detect all occurrences of the two most frequently appearing types of transcription errors, namely altering one single digit, and transposing two adjacent digits (including the transposition of the trailing check digit and the preceding digit).