  Machine learning for text classification is the underpinning of document
cataloging, news filtering, document steering and exemplification. In text
mining realm, effective feature selection is significant to make the learning
task more accurate and competent. One of the traditional lazy text classifier
k-Nearest Neighborhood (kNN) has a major pitfall in calculating the similarity
between all the objects in training and testing sets, there by leads to
exaggeration of both computational complexity of the algorithm and massive
consumption of main memory. To diminish these shortcomings in viewpoint of a
data-mining practitioner an amalgamative technique is proposed in this paper
using a novel restructured version of kNN called AugmentedkNN(AkNN) and
k-Medoids(kMdd) clustering.The proposed work comprises preprocesses on the
initial training set by imposing attribute feature selection for reduction of
high dimensionality, also it detects and excludes the high-fliers samples in
the initial training set and restructures a constrictedtraining set. The kMdd
clustering algorithm generates the cluster centers (as interior objects) for
each category and restructures the constricted training set with centroids.
This technique is amalgamated with AkNNclassifier that was prearranged with
text mining similarity measures. Eventually, significantweights and ranks were
assigned to each object in the new training set based upon their accessory
towards the object in testing set. Experiments conducted on Reuters-21578 a UCI
benchmark text mining data set, and comparisons with traditional kNNclassifier
designates the referredmethod yieldspreeminentrecitalin both clustering and
classification.
