Temporal Clustering
Time Element in Clustering
Clustering is an unsupervised machine-learning method whose goal is to find natural groupings (clusters) of instances in data sets.
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Clustering is an unsupervised machine-learning method whose goal is to find natural groupings (clusters) of instances in data sets.
Temporal and sequence classification is an automatic system that assigns one of the predefined classes to the time series or sequence input
Spiliopoulou et al. introduced the monic model, which finds cluster transition over accumulating data sets, providing an ageing function for clustering data that prioritises
External criteria validate the results of clustering based on some predefined structures of the data which is provided from an external source.
Internal criteria measure the 'goodness' of clusters for the data by extracting information from data and clusters alone, such as the compactness of data points inside one cluster.
Many clustering methods exist to be used in different situations according to the underlying data to be analysed and clustered.
Hierarchical clustering is a method to group instances of a data set into a series of nested clusters or a tree of clusters called a dendrogram
Fuzzy sets are used in fuzzy logic and can be considered as a generalisation of set theory. An element can be a member of a particular set or not in set theory
Centroid-based or representative-based clustering is a method of finding the best k clusters of items in the D data set.
Unsupervised machine learning methods aim to find patterns or groups (clusters) in data sets so that the most similar items in the data set will be gathered in the same cluster