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Cluster analysis is a statistical method used to group similar objects into clusters, aiding in data exploration and decision-making. It's crucial in fields like marketing, bioinformatics, and social sciences. Techniques like K-Means and hierarchical clustering help analyze large datasets, while similarity measures ensure accurate groupings. Its applications range from healthcare to urban planning, highlighting its versatility and importance in various sectors.
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Cluster analysis is a statistical technique that groups objects based on their similarities
Cluster analysis is an example of unsupervised learning, as it does not rely on pre-labeled data
Cluster analysis is widely applied in various fields such as marketing, bioinformatics, and social sciences
Similarity measures are pivotal in cluster analysis and include Euclidean distance, Manhattan distance, and Cosine similarity
The selection of a similarity measure greatly affects the clustering outcome and should be based on the data and analysis goals
K-Means clustering partitions data into a pre-defined number of clusters and aims to minimize within-cluster variance
Hierarchical clustering creates a hierarchy of clusters using either an agglomerative or divisive approach
Cluster analysis is employed in various sectors such as healthcare, retail, urban planning, and social media for data analysis and insights
Cluster analysis is instrumental in customer segmentation for targeted marketing and efficient resource use
Cluster analysis assists in categorizing students or institutions based on performance metrics or behaviors for tailored educational strategies and policy development
Cluster analysis plays a crucial role in exploratory data analysis, enabling the discovery of patterns and testing of hypotheses
00
As an example of ______ learning, cluster analysis does not use ______ data to form groups.
unsupervised
pre-labeled
01
Euclidean distance in cluster analysis
Geometric distance in multidimensional space; used to measure straight-line distance between points.
02
Manhattan distance usage
Sum of absolute differences of coordinates; useful for grid-based clustering.
