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What is Hybrid Clustering

Handbook of Research on Public Information Technology
Hybrid clustering is a clustering process that partitions a data set into preliminary clusters and then constructs a hierarchical structure upon these subclusters based on a given similarity measure.
Published in Chapter:
Categorization of Data Clustering Techniques
Baoying Wang (Waynesburg University, USA), Imad Rahal (College of Saint Benedict, Saint John’s University, USA), and Richard Leipold (Waynesburg University, USA)
Copyright: © 2008 |Pages: 10
DOI: 10.4018/978-1-59904-857-4.ch052
Data clustering is a discovery process that partitions a data set into groups (clusters) such that data points within the same group have high similarity while being very dissimilar to points in other groups (Han & Kamber, 2001). The ultimate goal of data clustering is to discover natural groupings in a set of patterns, points, or objects without prior knowledge of any class labels. In fact, in the machine-learning literature, data clustering is typically regarded as a form of unsupervised learning as opposed to supervised learning. In unsupervised learning or clustering, there is no training function as in supervised learning. There are many applications for data clustering including, but not limited to, pattern recognition, data analysis, data compression, image processing, understanding genomic data, and market-basket research.
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