Monitor and trace changes in clustering solutions of accumulating datasets 
    at successive time points. The clusters can adopt External and Internal transition at 
    succeeding time points. The External transitions comprise of Survived, Merged, Split, 
    Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes 
    in location and cohesion of the survived clusters. The package uses MONIC framework developed by 
    Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006)<doi:10.1145/1150402.1150491> .

A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <DOI:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <DOI:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.

Deciding what resolution to use can be a difficult question when
    approaching a clustering analysis. One way to approach this problem is to
    look at how samples move as the number of clusters increases. This package
    allows you to produce clustering trees, a visualisation for interrogating
    clusterings as resolution increases.

Returns an edit-distance based clusterization of an input vector of strings.
    Each cluster will contain a set of strings w/ small mutual edit-distance
    (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by
    stringdist::stringdist(). The set of all mutual edit-distances is then used by
    graph algorithms (from package 'igraph') to single out subsets of high connectivity.