Computer Science
University of the Cumberlands Clustering Algorithm Essay

University of the Cumberlands

Question Description

I’m studying for my Computer Science class and don’t understand how to answer this. Can you help me study?

For this assignment you will write a 5-page APA compliant paper that compares K-Means Clustering and Hierarchical Clustering. You will discuss when each is appropriate and the strengths and weaknesses of each algorithm. The page count does not include the title page nor the references page. You will be held to a maximum SafeAssign matching of 10%.

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Final Answer

Attached.

Running Head: CLUSTERING ALGORITHM

Clustering Algorithm
Institutional Affiliation
Date

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CLUSTERING ALGORITHM

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Introduction
When doing cluster analysis, the data provided are not labeled, meaning that the data will be
grouped based on its characteristics. Clustering entails dividing a population or data into a set of
groups to ensure the data points with the same traits are in the same group, than those in other
groups. A significant objective for clustering is to group the data based on their characteristics
and assign them into clusters. Clustering is a superior method used in data analysis to get an
intuition into the data (Elankavi, Kalaiprasath & Udayakumar, 2017). It is done based on the data
features in which the samples of the data are analyzed based on their characteristics.
Clustering is classified as an unsupervised learning method as the background of the data or
population. It can be used to compare the clustering algorithm output to the actual labels for
evaluation. It is intended to investigate the data structure by grouping the data points in different
categories (Elankavi, Kalaiprasath & Udayakumar, 2017). Clustering can be applied in diverse
areas such as in market segmentation, whereby customers are grouped in terms of the similarities
that exist between them, either based on their behaviors or compression. The primary cluster
methods or algorithms to be compared in this assignment are K-Means Clustering and
Hierarchical Clustering.
K-Means Clustering
K-means clustering is an algorithm in clustering and can be used to solve eminent clustering
problems. The technique ensures that the inter-cluster data points are the same while the cluster
is kept as far as possible. The data points are dispersed to a cluster, in which the entirety of the
squared distance amid the data points and the centroid of the cluster are at a minimum. When
there is less variation in the clusters, the more similar data points are within the cluster. The
procedure used in this method is based on classifying the available data set to absolute cluster

CLUSTERING ALG...

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