Straight Forward:
K Mean Clustering:
Hierarchal Clustering:
K Mean Clustering:
- In K-Mean clustering we tel machine about how much cluster we want to have.
- We start with defining with random cluster center and find new cluster center, continuing this same process we'll reach to a stage where our "new cluster centre" and "current cluster centre" will be same. This mean our algorithm is optimise and it is the cluster centre.
Hierarchal Clustering:
- In this clustering algorithm of Machine learning we feed data to machine and let machine decide how many cluster it want to group that data.
- We start with considering very data point as cluster centre and take mean of all nearby data points (depending upon radius you have selected). This process will continue until we optimise our algorithm (using mean shift algorithm) and at the point where we found our convergence is out cluster center.
This article comparing K-Means clustering and hierarchical clustering provides a very clear understanding of unsupervised machine learning techniques used for data grouping and pattern discovery. Understanding the differences between clustering approaches helps students choose suitable algorithms for analytics, recommendation systems, and intelligent data analysis applications. Learners interested in practical implementation concepts can also explore Clustering Projects to understand how clustering algorithms are applied in real-world machine learning systems.
ReplyDeleteClustering techniques are widely used in customer segmentation, anomaly detection, image analysis, and predictive analytics because of their ability to identify hidden structures in datasets. Students looking to strengthen their knowledge in intelligent algorithms can further refer to Machine Learning Algorithm Projects for ideas related to machine learning, data mining, and advanced analytical model development. This post gives a useful comparison of two important clustering methodologies.
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