Cluster Analysis in Data Science
                     Cluster Analysis in Data 
Science
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What is cluster analysis in data science?
Cluster analysis is a statistical method used to group similar objects into respective 
categories. It is also known as taxonomy analysis, segmentation analysis, and 
clustering. It is based on the method of grouping or categorizing data points in a 
certain dataset. It classifies data into distinct groups called clusters based on shared 
characteristics.
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Why is cluster analysis used?
In order to maximize the dissimilarity between different clusters and the similarity of the 
observations inside a specific cluster, cluster analysis is used.
Types of cluster analysis:
• k-means clustering: In data mining and statistics, it is a technique for cluster 
analysis. The goal is to divide a set of observations into a certain number of clusters 
(k), which divides the data into Voronoi cells.
• Hierarchical cluster analysis: An algorithm hierarchical cluster analysis or 
hierarchical clustering divides objects into clusters based on their similarities. The 
result is a collection of clusters, each of which differs from the others while having 
things that are generally similar to one another.
Is cluster analysis supervised or unsupervised?
Cluster analysis is an unsupervised method that is used when there is no known 
association between the observations and the outcome (target) variable, which is the 
case with unlabeled data.
Types of data in cluster analysis:
• Binary Variables
• Interval-Scaled Variables
• Nominal or Categorical Variables
• Ordinal Variables
• Variables Of Mixed Type
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Applications of cluster analysis:
Numerous fields, including biology, medicine, market research, and education, can 
benefit from cluster analysis.
• Image segmentation
• Market segmentation
• Object recognition
• Computing distances
Benefits of cluster analysis:
• It is a straightforward process.
• Its approach is simple.
• It is incredibly efficient.
• It is a less complex method.
• We can simply group the data using data visualization.
• It provides automatic recovery from failure.
Disadvantages of cluster analysis:
• It needs several clusters in advance.
• It has problems with categorical variables.
• It is unable to restore a corrupted database.
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Data Science with InfosecTrain
Any firm that needs to discover distinct groups of consumers, sales transactions, 
or other types of behaviors and items can use cluster analysis as a valuable 
data-mining technique. Data is omnipresent and a considerable part of our lives. 
You can join InfosecTrain's Data Science with Python and R training course if you 
want to learn more about cluster analysis in-depth and how to apply it 
effectively.
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