الفهرس 
An Enhanced Approach for None-Parametric Machine Learning Classifiers
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Abstract
K-nearest Neighbors (KNN) the state of art algorithm, classified as a member
of the powerfull algorithms that used in classification and regression predictions, KNN is one of the most generally utilized part of software engineering
these days. It is utilized by numerous enterprises for robotizing undertakings
and doing complex information examination. However, KNN has a weakness
points that made this popular classifier to be considered as a lazy classifier
that didn’t use it’s training set in computing or learning rather than storing
or memorizing. Which means that prediction stage will be very costly in
resources and time regarding large data-sets. For the KNN popularity and
wide range of use, many contributions that targeting increasing the classifications efficiency worked on (KNN) and achieved milestones on enhancing
the use of KNN methods. Here we present a new Algorithm (KMKNN) that
will enhance the classification methods of (KNN) in terms of improves the
performance of KNN in terms of prediction performance and time efficiency
with the help of the unsupervised approaches clustering algorithms, that
targeting the noticble efficiency results in which will save wastage of resources
and time consuming during predication stage.