The function extracts a set of representative objects that exhibits the key features of the whole data set, the goal being to get easy sounded interpretation of the latter. The user can set either the desired coverage level (the proportion of objects having a representative in their neighborhood) or the desired number of representatives.
dissrep(diss, criterion = "density", score = NULL, decreasing = TRUE,
coverage = 0.25, nrep = NULL, pradius = 0.10, dmax = NULL,
weights = NULL, trep, tsim)An object of class diss.rep. This is a vector containing
the indexes of the representative objects with the following additional attributes:
vector with the representative score of each object given the chosen criterion.
matrix with the distance of each object to its nearest representative.
vector with, for each object, the representative that represents it.
list with indexes of occurrences of each representative in original data.
a data frame with quality measures for each representative: number of objects assigned to the representative, number of objects in the representative's neighborhood, mean distance to the representative.
overall quality measure.
Print and summary methods are available.
A dissimilarity matrix or a dist object (see dist)
the representativeness criterion for sorting the
candidate list. One of "freq" (frequency), "density"
(neighborhood density) or "dist" (centrality). An optional
vector containing the scores for sorting the candidate objects may
also be provided. See below and details.
an optional vector containing the representativeness scores used for sorting the objects in the candidate list. The length of the vector must be equal to the number of rows/columns in the distance matrix, i.e the number of objects.
logical. If a score vector is provided, should
the objects in the candidate list be sorted in ascending order of the score. If FALSE, sort is in descending order. The first object in the candidate list is supposed to be the most representative.
controls the size of the representative set by setting
the desired coverage level, i.e the proportion of objects having a
representative in their neighborhood. Neighborhood radius is defined
by pradius.
number of representatives. If NULL (default),
coverage argument is used to control the size of the
representative set.
neighborhood
radius as a percentage of the maximum (theoretical)
distance dmax. Defaults to 0.1 (10%). Object \(y\) is
redundant to object \(x\) when it is in the neighborhood of \(x\), i.e., within a distance
pradius*dmax from \(x\).
maximum theoretical distance. The dmax value is used to derive the neighborhood radius as pradius*dmax. If NULL, the value of dmax is derived from the dissimilarity matrix.
vector of weights of length equal to the number of rows of the dissimilarity matrix. If NULL, equal weights are assigned.
Deprecated. Use coverage instead.
Deprecated. Use pradius instead.
Alexis Gabadinho and Gilbert Ritschard
The representative set is obtained by an heuristic. Representatives are selected by successively extracting from the sequences sorted by their representativeness score those which are not redundant with already retained representatives. The selection stops when either the desired coverage or the wanted number of representatives is reached. Objects are sorted either by the values provided as score argument, or by specifying one of the following as criterion argument: "freq" (sequence frequency), "density"
(neighborhood density), "dist"
(centrality).
The frequency criterion uses the frequencies as representativeness score. The frequency of an object in the data is computed as the number of other objects with whom the dissimilarity is equal to 0. The more frequent an object the more representative it is supposed to be. Hence, objects are sorted in decreasing frequency order. This criterion is equivalent to the neighborhood (see below) criterion with a neighborhood radius equal to 0.
The neighborhood density is the
number---density---of objects in the neighborhood of the object. This requires to set the neighborhood radius pradius. Objects are
sorted in decreasing density order.
The centrality criterion is the sum of distances to all other objects. The smallest the sum, the most representative the object.
Use criterion="dist" (centrality) and nrep=1 to get the medoid and criterion="density" and nrep=1 to get the densest object pattern.
For more details, see Gabadinho and Ritschard, 2013.
Gabadinho A, Ritschard G (2013). "Searching for typical life trajectories applied to child birth histories", In R Lévy, E. Widmer (eds.), Gendered Life Courses, pp. 287-312. Vienna: LIT.
Gabadinho A, Ritschard G, Studer M, Müller NS (2011). "Extracting and Rendering Representative Sequences", In A Fred, JLG Dietz, K Liu, J Filipe (eds.), Knowledge Discovery, Knowledge Engineering and Knowledge Management, volume 128 of Communications in Computer and Information Science (CCIS), pp. 94-106. Springer-Verlag.
seqrep, disscenter
## Defining a sequence object with the data in columns 10 to 25
## (family status from age 15 to 30) in the biofam data set
data(biofam)
biofam.lab <- c("Parent", "Left", "Married", "Left+Marr",
"Child", "Left+Child", "Left+Marr+Child", "Divorced")
biofam.seq <- seqdef(biofam[, 10:25], labels=biofam.lab)
## Computing the distance matrix
costs <- seqsubm(biofam.seq, method="TRATE")
biofam.om <- seqdist(biofam.seq, method="OM", sm=costs)
## Representative set using the neighborhood density criterion
biofam.rep <- dissrep(biofam.om)
biofam.rep
summary(biofam.rep)
## indexes of first occurrence of second representatives in original data
attr(biofam.rep,"idx.rep")[[2]][1]
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