摘要

Clustering is an unsupervised learning datamining technique. The principle of clustering depends on the concept of Distance Metric or Similarity Metric. Because of the variety of feature types and scales, the distance measure (or measures) must be chosen carefully. Edge weight in the graph finds the information for dissimilarity value between two data objects in the given database. A survey on clustering schema using minimum spanning tree (MST) approach is discussed in this paper. A graph-based clustering method is particularly well suited for dealing with data that is used in the construction of MST. It can be used for detecting clusters of any size and shape without specifying the actual number of clusters.

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