Cluster analysis is a popular technique in data analysis and exploration that finds similarities between different groups of data, based on which datasets are classified or segmented into predefined clusters. It is an unsupervised machine learning algorithm and doesn’t need data that has been previously categorised or labelled. Instead, the algorithm identifies patterns and structures within the data on its own. Cluster analysis is widely used in various fields such as marketing, biology, finance, and social sciences for tasks like customer segmentation, anomaly detection, pattern recognition, etc.
Python, with its extensive libraries such as scikit-learn, SciPy, and PyClustering, provides a robust platform for implementing cluster analysis algorithms effortlessly. Its simplicity, versatility, and rich ecosystem make Python well-suited for conducting cluster analysis and interpreting complex datasets. Additionally, Python’s readability and ease of use contribute to its popularity in the machine learning community.
The versatility of cluster analysis makes it indispensable for uncovering hidden structures and relationships in data. This helps analysts to derive actionable insights and make datadriven decisions, as shown in Table 1.
K-means clustering
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