Data clustering techniques PPT covers over view about different types of clustering methods used and explanation about their methods.
Data Clustering means collection of useful data into the groups. These groups are called clusters. It means classification of similar type of data. This reduces the complexity of the data and also reduces the number of bits required in the computer science. Clustering also includes pattern representation, extraction and selection. Clustering also depends on the data representation and if the data is good clusters will be compact and isolated. Sometimes the number of clusters is defined automatically; it is also the problem in clustering. There are many types of clustering:
Hierarchical clustering: this method is implemented on the 2-d types of data. They are mostly used in the pattern clustering. In which distance between the two clusters are minimum distance and rest all the pattern are drawn from these two clusters. In this each pattern we draw is own cluster.
Partition clustering: it generated single cluster instead of structure. They are best for the large data sets. They also work on isolated and compact clusters type of errors. They are easy to implement and less time consuming.
Nearest neighbor clustering: in this nearest node represent the basis of clustering. Each unlabeled pattern is assigned its nearest pattern.
Fuzzy clustering: in this every pattern belongs to one and only one cluster or belongs to the member function.
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