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An Improvement in K-mean Clustering Algorithm Using Better Time and Accuracy

Authors: Er. Nikhil Chaturvedi; Er. Anand Rajavat;

An Improvement in K-mean Clustering Algorithm Using Better Time and Accuracy

Abstract

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The process of k means algorithm data is partitioned into K clusters and the data are randomly choose to the clusters resulting in clusters that have the same number of data set. This paper is proposed a new K means clustering algorithm we calculate the initial centroids systemically instead of random assigned due to which accuracy and time improved.

Keywords

Data mining, Cluster, Basic K-means algorithm, Improved K-means algorithm, :Data mining, Cluster, Basic K-means algorithm, Improved K-means algorithm

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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